Object Path Prediction Method, Apparatus, and Program, and Automatic Operation System

ABSTRACT

An object path prediction method, apparatus, and program and an automatic operation system that can secure safety even in situations that can actually occur are provided. For this purpose, a computer having a storage unit that stores the position of an object and the internal state including the speed of the object reads the position and internal state of the object from the storage unit, generates trajectories in a space-time consisting of time and space from changes of the positions that can be taken by the object with the passage of time based on the read position and internal state of the object, and predicts probabilistically paths of the object by using the generated trajectories.

TECHNICAL FIELD

The present invention relates to an object path prediction method,apparatus, and program that predict a path of an object based on aposition and an internal state of the object, and an automatic operationsystem.

BACKGROUND ART

In recent years, various attempts have been made to realize an automaticoperation of a movable body such as a four-wheel vehicle. Forrealization of an automatic operation of a movable body, it is importantto correctly detect objects such as vehicles, pedestrians, and obstaclespresent around the movable body and to avoid danger while running basedon the detection results. Of these two factors, an object detectiontechnology using various sensors and radars is known as a technology toprecisely detect surrounding objects.

An automatic operation technology of a movable body is a technology bywhich the movable body is automatically moved from an origin to adestination only by entering the destination. When the range of movementis narrow, this technology can be realized in a path finding technologyby creating a map of the range of movement in advance and predicting aninfluence of dynamic obstacles in advance. However, when the range ofmovement of a movable body is wide such as when the movable body is anautomobile, the automatic operation technology cannot be realized in thepath finding technology. The wide range here is a range in which a timet needed for avoiding a dynamic obstacle and a time X needed for runningan entire distance are vastly different and, for example, is a case inwhich τ is several hours while t is several seconds.

If the range of movement of a movable body is wide, there are mainly tworeasons why the automatic operation technology is not attributable tothe path finding technology. First, a first reason is as follows:Consider, for example, a situation when a time of about 10 t elapsesafter a movable body starts from an origin. In this case, an influenceof dynamic obstacles spreads out over an entire road and a path on whichno collision occurs cannot be defined. That is, if the range of movementof a movable body is wide, a path from an origin to a destination cannotbe calculated in advance.

Next, a second reason is as follows: If the range of movement of amovable body is wide, as described above, the time τ needed for runningan entire distance is much longer than t. Thus, it is impossible for acomputer mounted on an automobile to complete required calculationwithin a practical time in which evasion of actual collision can berealized.

In the automatic operation technology of a movable body that moves in awide range such as an automobile, as described above, in addition to thepath finding technology in which an influence of at least other dynamicobstacles is not considered or an influence thereof is not practicallyneeded for calculation, a path calculation technology by whichcalculation needed for avoiding collision with dynamic obstacles iscompleted within a practical time to calculate a path to avoid dangerwhile running is needed.

As a technology to avoid danger while running of the path calculationtechnology described above, a technology is known by which, in a systemconsisting of a plurality of objects and subject vehicle, paths of eachobject including subject vehicle are generated by using informationconcerning the position and velocity of subject vehicle and informationconcerning the positions and velocities of the plurality of objectsexcluding subject vehicle to predict possibilities of any two objectsconstituting the system to collide (See, for example, NonpatentLiterature 1). According to this technology, paths taken by all objectsconstituting the system are predicted by means of operation sequences ofthe same framework using the concept of probability and are output.Then, based on obtained prediction results, a path for realizing thesafest condition for the entire system including subject vehicle isdetermined and output.

Nonpatent Literature 1: A. Broadhurst, S. Baker, and T. Kanade, “MonteCarlo Road Safety Reasoning”, IEEE Intelligent Vehicle Symposium(IV2005), IEEE, (June, 2005)

DISCLOSURE OF INVENTION Problem to be Solved by the Invention

However, since the technology disclosed by Nonpatent Literature 1focuses on predicting a path that makes all objects constituting asystem safe, it is not certain whether or not the path obtained fromsuch a prediction sufficiently secures safety for a specific object(such as subject vehicle).

This point will be described more specifically. In actual roadsituations, a driver of another vehicle or a pedestrian may recognizeroad situations falsely, leading to unfavorable behavior to surroundingobjects including subject vehicle without a person in question beingaware of it. In contrast, Nonpatent Literature 1 tacitly assumes thatall objects exhibit behavior giving priority to safety and thus, it isnot clear whether safety can sufficiently be secured also in situationsthat can actually occur such as when some object behaves unfavorably tosurrounding objects.

The present invention has been made in view of the above circumstancesand an object thereof is to provide an object path prediction method,apparatus, and program and an automatic operation system that can securesafety even in situations that can actually occur.

Means for Solving Problem

To solve the problems as described above and to achieve objects, anobject path prediction method according to the present invention is anobject path prediction method for predicting a path of an object by acomputer having a storage unit that stores at least a position of theobject and an internal state including a speed of the object, includinga trajectory generation step of generating a trajectory in a space-timeconstituted by time and space from changes of the position that can betaken by the object with a passage of time based on a read position andinternal state of the object after reading the position and internalstate of the object from the storage unit, and a prediction step ofprobabilistically predicting the path of the object by using thetrajectory generated in the trajectory generation step.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, thetrajectory generation step includes an operation selection step ofselecting an operation performed on the object from a plurality ofoperations, an object operation step of causing the operation selectedin the operation selection step to be performed for a predeterminedperiod of time, and a determination step of determining whether or notthe position and the internal state of the object after the operationselected is performed in the object operation step satisfy controlconditions concerning control of the object and movement conditionsconcerning movable areas of the object, wherein a set of processing fromthe operation selection step to the determination step is repeatedlyperformed until a trajectory generation time in which the trajectory isgenerated has reached.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, theoperation selection step selects an operation in accordance with anoperation selection probability granted to each of the plurality ofoperations, and if, as a result of determination in the determinationstep, the position and the internal state of the object satisfy thecontrol conditions and the movement conditions, the time is set forwardbefore returning to the operation selection step.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, theoperation selection probability is defined by using random numbers.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, anumber of trajectories to be generated in the trajectory generation stepis previously set.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, if,as a result of determination in the determination step, the controlconditions and the movement conditions are satisfied, all selectableoperations are caused to be performed by setting forward the time andmaking a recursion.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, thestorage unit stores the position and the internal state of a pluralityof objects and the trajectory generation step generates trajectories ofeach of the plurality of objects in the space-time.

Further, the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, theprediction step specifies one object from the plurality of objects andcalculates existing probabilities in the space-time of objects otherthan the specified object.

Further, the object path prediction method according to the presentinvention, in an aspect of the present invention as described above,further includes an output step of outputting information containingprediction results in the prediction step.

Further, an object path prediction method according to the presentinvention is an object path prediction method for predicting paths of aplurality of objects by a computer having a storage unit that stores atleast positions of the plurality of objects and an internal stateincluding a speed of each object, and includes a trajectory generationstep of generating a trajectory in a space-time constituted by time andspace from changes of the position that can be taken by each of theplurality of objects with a passage of time based on, after reading thepositions and the internal states of the plurality of objects from thestorage unit, the read positions and the internal states of the objects,a prediction step of probabilistically predicting the paths of theplurality of objects by using the trajectories generated in thetrajectory generation step, and an interference level calculation stepof calculating, based on results of prediction in the prediction step, alevel of interference quantitatively showing an extent of interferencebetween the paths that can be taken by the specific object and thosethat can be taken by other objects.

Further, the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, theinterference level calculation step increases or decreases a value ofthe level of interference between the specific object and each of theother objects by a specified quantity in accordance with a number oftimes that the specific object and each of the other objects move closerthan an interference distance, which is a spatial distance at whichobjects interfere with each other.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, theinterference level calculation step increases, when the specific objectand one of the other objects move closer than the interference distance,the value of the level of interference between both objects movingcloser in proportion to a collision probability of the both objects inthe space-time.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, theinterference level calculation step increases, when the specific objectand one of the other objects move closer than the interference distance,the value of the level of interference between both objects movingcloser in proportion to a magnitude of a relative velocity at a timewhen the both objects move closer.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, thestorage unit stores a magnitude of a relative velocity during collisionbetween different objects by associating with a damage scale evaluationvalue for evaluating a scale of damage caused by the collision or anamount of damage losses caused by the collision and the interferencelevel calculation step reads, when the specific object and one of theother objects move closer than the interference distance, the damagescale evaluation value or the amount of damage losses in accordance withthe magnitude of the relative velocity at a time when both objects movecloser from the storage unit and increases the level of interferencebetween the both objects in proportion to the damage scale evaluationvalue or the amount of damage losses.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, theinterference level calculation step sets, when the time needed for thespecific object and one of the other objects to move closer than theinterference distance from an initial position of each object is smallerthan the value of the level of interference between the both objects,the time needed from the initial position as the value of the level ofinterference.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, theinterference level calculation step adds up the value of each level ofinterference between the specific object and the other objects byassigning weights.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, thetrajectory generation step includes an operation selection step ofselecting an operation performed on the object from a plurality ofoperations, an object operation step of causing the operation selectedin the operation selection step to be performed for a predeterminedperiod of time, and a determination step of determining whether or notthe position and the internal state of the object after the operationselectedis performed in the object operation step satisfy controlconditions concerning control of the object and movement conditionsconcerning movable areas of the object, wherein a set of processing fromthe operation selection step to the determination step is repeatedlyperformed until a trajectory generation time in which the trajectory isgenerated has reached.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, theoperation selection step selects an operation in accordance with anoperation selection probability granted to each of the plurality ofoperations, and if, as a result of determination in the determinationstep, the position and the internal state of the object satisfy thecontrol conditions and the movement conditions, the time is set forwardbefore returning to the operation selection step.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, theoperation selection probability is defined by using random numbers.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, anumber of trajectories to be generated in the trajectory generation stepis previously set.

Further, the object path prediction method according to the presentinvention, in an aspect of the present invention as described above,further includes an output step of outputting information in accordancewith the level of interference calculated in the interference levelcalculation step.

Further, the object path prediction method according to the presentinvention, in an aspect of the present invention as described above,further includes a path selection step of selecting a path to be takenby the specific object contained in the plurality of objects inaccordance with the level of interference calculated in the interferencelevel calculation step.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, thelevel of interference has a smaller value as an extent of interferencebetween paths that can be taken by the specific object and those thatcan be taken by the other objects decreases and the path selection stepselects a path whose level of interference is minimum.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, thepath selection step selects, when there is a plurality of paths whoselevel of interference is minimum, a path that best matches apredetermined additional selection criterion from the plurality ofpaths.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, thelevel of interference has a larger value as an extent of interferencebetween paths that can be taken by the specific object and those thatcan be taken by the other objects decreases and the path selection stepselects a path whose level of interference is maximal.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, thepath selection step selects, when there is a plurality of paths whoselevel of interference is maximal, a path that best matches apredetermined additional selection criterion from the plurality ofpaths.

Further, the object path prediction method according to the presentinvention, in an aspect of the present invention as described above,further includes an actuating signal transmission step of transmitting,after generating an actuating signal in accordance with a history ofpositions of a path selected in the path selection step and an operationsequence for realizing the path, the generated actuating signal to anoutside.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, theinterference level calculation step increases or decreases the value ofthe level of interference between the specific object and each of theother objects by a specified quantity in accordance with a number oftimes that the specific object and each of the other objects move closerthan an interference distance, which is a spatial distance at whichobjects interfere with each other.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, theinterference level calculation step adds up the value of each level ofinterference between the specific object and the other objects byassigning weights.

Further, in the object path prediction method according to the presentinvention, in an aspect of the present invention as described above, thetrajectory generation step includes an operation selection step ofselecting an operation performed on the object from a plurality ofoperations, an object operation step of causing the operation selectedin the operation selection step to be performed for a predeterminedperiod of time, and a determination step of determining whether or notthe position and the internal state of the object after the operationselected is performed in the object operation step satisfy controlconditions concerning control of the object and movement conditionsconcerning movable areas of the object, wherein a set of processing fromthe operation selection step to the determination step is repeatedlyperformed until a trajectory generation time in which the trajectory isgenerated has reached.

Further, the object path prediction method according to the presentinvention, in an aspect of the present invention, further includes anoutput step of outputting information concerning the path selected inthe path selection step.

An object path prediction apparatus according to the present inventionincludes a storage unit that stores at least a position of an object andan internal state including a speed of the object, a trajectorygeneration unit that generates a trajectory in a space-time constitutedby time and space from changes of the position that can be taken by theobject with a passage of time based on a read position and internalstate of the object after reading the position and the internal state ofthe object from the storage unit, and a prediction unit thatprobabilistically predicts the path of the object by using thetrajectory generated by the trajectory generation unit.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, the trajectory generation unit includes an operation selectionunit that selects an operation performed on the object from a pluralityof operations, an object operation unit that causes the operationselected by the operation selection unit to be performed for apredetermined period of time, and a determination unit that determineswhether or not the position and the internal state of the object afterthe operation selected is performed by the object operation unit satisfycontrol conditions concerning control of the object and movementconditions concerning movable areas of the object, wherein a set ofprocessing from operation selection processing by the operationselection unit to determination processing by the determination unit isrepeatedly performed until a trajectory generation time in which thetrajectory is generated has reached.

Further, the object path prediction apparatus according to the presentinvention, in an aspect of the present invention as described above, theoperation selection unit selects an operation in accordance with anoperation selection probability granted to each of the plurality ofoperations, and if, as a result of determination by the determinationunit, the position and the internal state of the object satisfy thecontrol conditions and the movement conditions, the time is set forwardbefore returning to the operation selection processing by the operationselection unit.

Further, the object path prediction apparatus according to the presentinvention, in an aspect of the present invention as described above, theoperation selection probability is defined by using random numbers.

Further, the object path prediction apparatus according to the presentinvention, in an aspect of the present invention, a number oftrajectories to be generated by the trajectory generation unit ispreviously set.

Further, the object path prediction apparatus according to the presentinvention, in an aspect of the present invention as described above, if,as a result of determination by the determination unit, the controlconditions and the movement conditions are satisfied, all selectableoperations are caused to be performed by setting forward the time andmaking a recursion.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, the storage unit stores the position and the internal state of aplurality of objects and the trajectory generation unit generatestrajectories of each of the plurality of objects in the space-time.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, the prediction unit specifies one object from the plurality ofobjects and calculates existing probabilities in the space-time ofobjects other than the specified object.

Further, the object path prediction apparatus according to the presentinvention, in an aspect of the present invention as described above,further includes an output unit that outputs information containingprediction results by the prediction unit.

An object path prediction apparatus according to the present inventionincludes a storage unit that stores at least positions of a plurality ofobjects and an internal state including a speed of each object, atrajectory generation unit that generates a trajectory in a space-timeconstituted by time and space from changes of the position that can betaken by each of the plurality of objects with a passage of time basedon, after reading the positions and the internal states of the pluralityof objects from the storage unit, the read positions and the internalstates of the objects, a prediction unit that probabilistically predictsthe paths of the plurality of objects by using the trajectoriesgenerated by the trajectory generation unit, and an interference levelcalculation unit that calculates, based on results of prediction by theprediction unit, a level of interference quantitatively showing anextent of interference between the paths that can be taken by thespecific object and those that can be taken by other objects.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, the interference level calculation unit increases or decreases avalue of the level of interference between the specific object and eachof the other objects by a specified quantity in accordance with a numberof times that the specific object and each of the other objects movecloser than an interference distance, which is a spatial distance atwhich objects interfere with each other.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, the interference level calculation unit increases, when thespecific object and one of the other objects move closer than theinterference distance, the value of the level of interference betweenboth objects moving closer in proportion to a collision probability ofthe both objects in the space-time.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, the interference level calculation unit increases, when thespecific object and one of the other objects move closer than theinterference distance, the value of the level of interference betweenboth objects moving closer in proportion to a magnitude of a relativevelocity at a time when the both objects move closer.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, the storage unit stores a magnitude of a relative velocity duringcollision between different objects by associating with a damage scaleevaluation value for evaluating a scale of damage caused by thecollision or an amount of damage losses caused by the collision and theinterference level calculation unit reads, when the specific object andone of the other objects move closer than the interference distance, thedamage scale evaluation value or the amount of damage losses inaccordance with the magnitude of the relative velocity at a time whenboth objects move closer from the storage unit and increases the levelof interference between the both objects in proportion to the damagescale evaluation value or the amount of damage losses.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, the interference level calculation unit sets, when the timeneeded for the specific object and one of the other objects to movecloser than the interference distance from an initial position of eachobject is smaller than the value of the level of interference betweenthe both objects, the time needed from the initial position as the valueof the level of interference.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, the interference level calculation unit adds up the value of eachlevel of interference between the specific object and the other objectsby assigning weights.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, the trajectory generation unit includes an operation selectionunit that selects an operation performed on the object from a pluralityof operations, an object operation unit that causes the operationselected by the operation selection unit to be performed for apredetermined period of time, and a determination unit that determineswhether or not the position and the internal state of the object afterthe operation selected is performed by the object operation unit satisfycontrol conditions concerning control of the object and movementconditions concerning movable areas of the object, wherein a set ofprocessing from operation selection processing by the operationselection unit to determination processing by the determination unit isrepeatedly performed until a trajectory generation time in which thetrajectory is generated has reached.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, the operation selection unit selects an operation in accordancewith an operation selection probability granted to each of the pluralityof operations, and if, as a result of determination by the determinationunit, the position and the internal state of the object satisfy thecontrol conditions and the movement conditions, the time is set forwardbefore returning to the operation selection processing by the operationselection unit.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, the operation selection probability is defined by using randomnumbers.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, a number of trajectories to be generated by the trajectorygeneration unit is previously set.

Further, the object path prediction apparatus according to the presentinvention, in an aspect of the present invention as described above,further includes an output unit that outputs information in accordancewith the level of interference calculated by the interference levelcalculation unit.

Further, the object path prediction apparatus according to the presentinvention, in an aspect of the present invention as described abovefurther includes a path selection unit that selects a path to be takenby the specific object in accordance with the level of interferencecalculated by the interference level calculation unit.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, the level of interference has a smaller value as an extent ofinterference between paths that can be taken by the specific object andthose that can be taken by the other objects decreases and the pathselection unit selects a path whose level of interference is minimum.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, the path selection unit selects a path that best matches apredetermined additional selection criterion from the plurality of pathswhen there is a plurality of paths whose level of interference isminimum.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, the level of interference has a larger value as an extent ofinterference between paths that can be taken by the specific object andthose that can be taken by the other objects decreases and the pathselection unit selects a path whose level of interference is maximal.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, the path selection unit selects, when there is a plurality ofpaths whose level of interference is maximal, a path that best matches apredetermined additional selection criterion from the plurality ofpaths.

Further, the object path prediction apparatus according to the presentinvention, in an aspect of the present invention as described above,further includes an actuating signal transmission unit that transmits,after generating an actuating signal in accordance with a history ofpositions of a path selected by the path selection unit and an operationsequence for realizing the path, the generated actuating signal to anoutside.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, the interference level calculation unit increases or decreasesthe value of the level of interference between the specific object andeach of the other objects by a specified quantity in accordance with anumber of times that the specific object and each of the other objectsmove closer than an interference distance, which is a spatial distanceat which objects interfere with each other.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, the interference level calculation unit adds up the value of eachlevel of interference between the specific object and the other objectsby assigning weights.

Further, in the object path prediction apparatus according to thepresent invention, in an aspect of the present invention as describedabove, the trajectory generation unit includes an operation selectionunit that selects an operation performed on the object from a pluralityof operations, an object operation unit that causes the operationselected by the operation selection unit to be performed for apredetermined period of time, and a determination unit that determineswhether or not the position and the internal state of the object afterthe operation selected is performed by the object operation unit satisfycontrol conditions concerning control of the object and movementconditions concerning movable areas of the object, wherein a set ofprocessing from operation selection processing by the operationselection unit to determination processing by the determination unit isrepeatedly performed until a trajectory generation time in which thetrajectory is generated has reached.

Further, the object path prediction apparatus according to the presentinvention, in an aspect of the present invention as described above,further includes an output unit that outputs information concerning thepath selected by the path selection unit.

An object path prediction program according to the present inventioncauses the computer to perform the object path prediction methodaccording to any one of the aspects of the present invention describedabove.

An automatic operation system according to the prsent invention is anautomatic operation system for automatically operating a vehicle bybeing mounted on the vehicle, and includes the object path predictionapparatus according to any one of the aspects of the present inventiondescribed above, and an actuator apparatus that realizes a path selectedby the path selection unit provided in the object path predictionapparatus and operating the vehicle in accordance with an actuatingsignal.

EFFECT OF THE INVENTION

According to an object path prediction method, apparatus, and program,and an automatic operation system in the present invention, safety canbe secured even in situations that can actually occur.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram showing a functional configuration of anobject path prediction apparatus according to a first embodiment of thepresent invention;

FIG. 2 is a flowchart showing an overview of an object path predictionmethod according to the first embodiment of the present invention;

FIG. 3 is a flowchart showing the overview of trajectory generationprocessing by the object path prediction method according to the firstembodiment of the present invention;

FIG. 4 is a diagram schematically showing a trajectory generated in athree-dimensional space-time;

FIG. 5 is a diagram schematically showing a set of trajectoriesgenerated in the three-dimensional space-time;

FIG. 6 is an explanatory diagram schematically showing a configurationof a space-time environment formed by the object path prediction methodaccording to the first embodiment of the present invention;

FIG. 7 is a diagram showing a display output example of predictionresults by the object path prediction apparatus according to the firstembodiment of the present invention;

FIG. 8 is a diagram showing a display output example (second example) ofprediction results by the object path prediction apparatus according tothe first embodiment of the present invention;

FIG. 9 is a diagram schematically showing the configuration of aspace-time environment formed when a model that maintains an operationof subject vehicle is adopted;

FIG. 10 is a flowchart showing the overview of trajectory generationprocessing by an object path prediction method according to a secondembodiment of the present invention;

FIG. 11 is a flowchart showing details of trajectory generationprocessing by the object path prediction method according to the secondembodiment of the present invention;

FIG. 12 is a block diagram showing the functional configuration of anobject path prediction apparatus according to a third embodiment of thepresent invention;

FIG. 13 is a flowchart showing the overview of an object path predictionmethod according to the third embodiment of the present invention;

FIG. 14 schematically shows problems of conventional path predictioncalculations.

FIG. 15 is a diagram schematically showing advantages of path predictioncalculations by the object path prediction method according to the thirdembodiment of the present invention;

FIG. 16 is a flowchart showing details of interference level calculationprocessing by the object path prediction method according to the thirdembodiment of the present invention;

FIG. 17 is a diagram schematically showing a relationship between onetrajectory of subject vehicle and that of another vehicle in aspace-time;

FIG. 18 is a diagram exemplifying a function that gives time dependenceof interference between objects;

FIG. 19 is a flowchart showing details of interference level calculationprocessing by an object path prediction method according to a fourthembodiment of the present invention;

FIG. 20 is a flowchart showing details of interference level calculationprocessing by an object path prediction method according to a fifthembodiment of the present invention;

FIG. 21 is an explanatory diagram schematically showing anotherconfiguration of the space-time environment;

FIG. 22 is a block diagram showing the functional configuration of anautomatic operation system including an object path prediction apparatusaccording to a sixth embodiment of the present invention;

FIG. 23 is a flowchart showing the overview of an object path predictionmethod according to the sixth embodiment of the present invention;

FIG. 24 is a flowchart showing details of path selection processing bythe object path prediction method according to the sixth embodiment ofthe present invention;

FIG. 25 is a diagram showing a display output example of path settingresults by the object path prediction apparatus according to the sixthembodiment of the present invention;

FIG. 26 is a diagram showing a display output example (second example)of path setting results by the object path prediction apparatusaccording to the sixth embodiment of the present invention;

FIG. 27 is a flowchart showing details of interference level calculationprocessing by an object path prediction method according to a seventhembodiment of the present invention; and

FIG. 28 is a flowchart showing details of path selection processing bythe object path prediction method according to the seventh embodiment ofthe present invention.

EXPLANATIONS OF LETTERS OF NUMERALS

-   -   1, 101, 201 Object path prediction apparatus    -   2 Input section    -   3 Sensor section    -   4 Trajectory generation section (trajectory generation unit)    -   5, 105 Prediction section (prediction unit)    -   6, 107, 209 Output section (output unit)    -   7, 108, 210 Storage section (storage unit)    -   41 Operation selection part (operation selection unit)    -   42 Object operation part (object operation unit)    -   43 Determination part (determination unit)    -   51 Prediction calculation part    -   52 Image generation part    -   61, 171, 291 Display part    -   106 Interference level calculation part (interference level        calculation unit)    -   172, 292 Warning beep generation part    -   207 Path selection part (path selection unit)    -   208 Actuating signal transmission part (actuating signal        transmission unit)    -   211 Actuator apparatus    -   1000 Automatic operation system    -   B₁, B₂, B₃ Path    -   CN Display screen    -   D_(a), D_(b) Area    -   Env(P₁, P₂), Env′ (P₁, P₂), Env(P₁, P₂, P₃) Space-time        environment    -   F Front glass    -   H Arrow    -   O₁, O₂, O₃ Object    -   R, Rd Road    -   ST Steering

BEST MODES FOR CARRYING OUT THE INVENTION

Best modes for carrying out the present invention (hereinafter, referredto as “embodiments”) will be described below with reference to attacheddrawings.

First Embodiment

FIG. 1 is a block diagram showing a functional configuration of anobject path prediction apparatus according to a first embodiment of thepresent invention. An object path prediction apparatus 1 shown in thefigure is an apparatus mounted on a movable body such as a four-wheelvehicle to detect objects present within a predetermined range aroundsubject vehicle and to predict paths of detected objects and subjectvehicle.

The object path prediction apparatus 1 includes an input section 2 intowhich various kinds of information are input from outside, a sensorsection 3 for detecting positions and internal states of objects presentwithin a predetermined range, a trajectory generation section 4 forgenerating a trajectory in space-time constituted by time and space fromchanges of the positions that can be taken by an object with the passageof time based on results detected by the sensor section 3, a predictionsection 5 for making a probabilistic prediction about the path of theobject using the trajectory generated by the trajectory generationsection 4, an output section 6 for outputting various kinds ofinformation including at least results of predictions made by theprediction section 5, and a storage section 7 for storing informationincluding trajectories in space-time generated by the trajectorygeneration section 4 and results of predictions made by the predictionsection 5.

The input section 2 has a function of entering various kinds of settinginformation and the like for predicting paths of objects and is realizedby using a remote controller, a keyboard (including a touch panel typeon which input operations can be performed on the screen), a pointingdevice (such as a mouse and a trackpad) and the like. A microphonethrough which entries of information by voice can be made may beprovided as the input section 2. If various kinds of setting informationare preset, the storage section 7 having a ROM (Read Only Memory) or thelike in which such information is stored may substitute for the functionof the input section 2.

The sensor section 3 is realized by using a millimeter wave radar, alaser radar, an image sensor or the like. The sensor section 3 hasvarious kinds of sensors such as a speed sensor, an acceleration sensor,a rudder angle sensor, and an angular velocity sensor, and can alsodetect a moving situation of subject vehicle. An internal state of anobject detected by the sensor section 3 is a useful state that can beused for predictions of the object and is preferably a physical quantitysuch as a velocity (having a speed and a direction) and an angularvelocity (having a magnitude and a direction). Naturally, a case inwhich such physical quantities take the value of 0 (a state in which theobject is at rest) is also included.

The trajectory generation section 4 predicts and generates trajectoriesthat an object can follow before a predetermined time elapses and has anoperation selection part 41 for selecting an operation for causing anobject to virtually move in a simulation from a plurality of operations,an object operation part 42 for performing the operation selected by theoperation selection part 41 for a predetermined period of time, and adetermination part 43 for determining whether or not the position andinternal state of the object after the operation by the object operationpart 42 satisfy predetermined conditions.

The prediction section 5 has a prediction calculation part 51 forperforming probabilistic prediction calculations by using a trajectoryof each object output from the trajectory generation section 4 and animage generation part 52 for generating images to be displayed andoutput by the output section 6 in accordance with results of predictioncalculations by the prediction calculation part 51.

The output section 6 has a display part 61 for displaying and outputtingan image generated by the image generation part 52 in the predictionsection 5. The display part 61 is realized by using a display of liquidcrystal, plasma, electroluminescence and the like. In the firstembodiment, a projector is set up in an upper part behind a driver'sseat as the display part 61. The projector has a function that allows adisplay by superimposition on the front glass of a four-wheel vehicle. Aspeaker outputting voice information to the outside may be provided asthe output section 6.

The storage section 7 stores, in addition to detection results by thesensor section 3, trajectories generated by the trajectory generationsection 4, prediction results by the prediction section 5, operationsselected by the operation selection part 41 in the trajectory generationsection 4 and the like. The storage section 7 is realized by using a ROMin which a program for starting a predetermined OS (Operation System),an object path prediction program according to the first embodiment andthe like are stored in advance and a RAM (Random Access Memory) in whichoperation parameters, data and the like are stored. The storage section7 can also be realized by providing for the object path predictionapparatus 1 an interface into which a computer-readable recording mediumcan be equipped and equipping a recording medium corresponding to theinterface.

The object path prediction apparatus 1 having the above functionalconfiguration is an electronic apparatus (computer) provided with a CPU(Central Processing Unit) having operational and control functions. TheCPU provided with the object path prediction apparatus 1 performsprocessing for the object path prediction method according to the firstembodiment by reading information stored in the storage section 7 andvarious programs including the object path prediction program from thestorage section 7. The object path prediction program according to thefirst embodiment can widely be distributed by recording the object pathprediction program in a computer readable recording medium such as ahard disk, flexible disk, CD-ROM, DVD-ROM, flash memory, MO disk and thelike.

Next, the object path prediction method according to the firstembodiment of the present invention will be described. FIG. 2 is aflowchart showing a process overview of the object path predictionmethod according to the first embodiment. In a description that follows,all objects to be predicted are assumed to move on a two-dimensionalplane.

First, the sensor section 3 detects positions and internal states ofobjects within a predetermined range with respect to subject vehicle andstores detected information in the storage section 7 (step S1).Hereinafter, it is assumed that the position of an object is denoted byvalues of the center of the object and the internal state of an objectis specified by a velocity (speed v, direction θ). At step S1, theinternal state of subject vehicle is also naturally detected and storedin the storage section 7.

Next, the trajectory generation section 4 generates trajectories foreach object by using detection results input by the sensor section 3(step S2). FIG. 3 is a flowchart showing details of trajectorygeneration processing by the trajectory generation section 4. In thefigure, it is assumed that the total number of objects (includingsubject vehicle) detected by the sensor section 3 is K and an operationto generate a trajectory for one object O_(k) (1≦k≦K, k is a naturalnumber) is performed N_(k) times (in this sense, both K and N_(k) arenatural numbers). The time during which a trajectory is generated(trajectory generation time) is assumed to be T (>0).

In the first embodiment, changes of the outside world such as paths ofother vehicles can be predicted within a practical calculation time bysetting the trajectory generation time T (and an operation time Δtdescribed later) appropriately. This applies also to other embodimentsof the present invention.

First, the value of a counter k to identify objects is initialized to 1and also a counter n_(k) indicating the number of times of trajectorygeneration for the same object O_(k) is initialized to 1 (step S201).Hereinafter, processing will be described by referring to the ordinaryobject O_(k).

Next, the trajectory generation section 4 reads results detected by thesensor section 3 from the storage section 7 and sets the read detectionresults as an initial state (step S202). More specifically, the time tis set to 0 and the initial position (x_(k)(0), y_(k)(0)) and theinitial internal state (v_(k)(0), θ_(k)(0)) are set to input information(x_(k0), y_(k0)) and (v_(k0), θ_(k0)) from the sensor section 3respectively.

Subsequently, the operation selection part 41 selects an operationu_(k)(t) to be performed in the following time Δt from the plurality ofselectable operations in accordance with an operation selectionprobability attached in advance to each operation (step S203). Theoperation selection probability p(u_(kc)) to select the operation u_(kc)is defined, for example, by associating elements of a set {u_(kc)} ofselectable operations as u_(k)(t) and predetermined random numbers. Inthis sense, a different operation selection probability p(u_(kc)) may begranted to each operation u_(kc) or an equal probability may be grantedto all elements in the operation set {u_(kc)}. In the latter case,p(u_(kc))=1/(the number of all selectable operations) holds. It is alsopossible to define the operation selection probability p(u_(kc)) as afunction dependent on the position and internal state of subject vehicleand also a surrounding road environment.

The operation u_(kc) is generally constituted by a plurality of elementsand content of selectable operations depends on the type of objectO_(k). If, for example, the object O_(k) is a four-wheel vehicle, theacceleration or angular velocity of the four-wheel vehicle is determinedby how the steering wheel is turned or the accelerator is stepped on. Inconsideration of this, the operation u_(kc) performed on the objectO_(k), which is a four-wheel vehicle, is determined by elementsincluding acceleration and angular velocity. In contrast, if the objectO_(k) is a person, the operation u_(kc) can be specified by a speed.

A more concrete setting example of the operation u_(kc) will be given.If the object O_(k) is an automobile, the acceleration is set in therange of −10 to +30 (km/h/sec) and the steering angle in the range of −7to +7 (deg/sec) (in both cases, the direction is specified by the sign).If the object O_(k) is a person, the speed is set in the range of 0 to+36 (km/h) and the direction in the range of 0 to 360 (deg). Quantitiesdescribed here are all continuous quantities. In such a case, the numberof elements of each operation may be made finite by performingappropriate discretization to constitute a set {u_(kc)} of eachoperation.

Then, the object operation part 42 causes the operation u_(kc) selectedat step S203 to perform for the time Δt (step S204). The time Δt ispreferably small in terms of precision, but may practically be a valueof about 0.1 to 0.5 (sec). In a description that follows, the trajectorygeneration time T is assumed to be an integral multiple of Δt, however,the value of T may be variable in accordance with the speed of theobject O_(k) and may not be an integral multiple of Δt.

Subsequently, the determination part 43 determines whether or not theinternal state of the object O_(k) after causing the operation u_(kc) tobe performed at step S204 satisfies predetermined control conditions(step S205) and also determines whether or not the position of theobject O_(k) after causing the operation u_(kc) to be performed iswithin a movable area (step S206). The control conditions used fordetermination at step S205 are determined in accordance with the type ofobject O_(k) and, if, for example, the object O_(k) is a four-wheelvehicle, are determined by the range of speed after the operation atstep S204, a vehicle G of maximal acceleration after the operation atstep S204 and the like. The movable area determined at step S206, on theother hand, refers to an area of roads (including roadways and footways)and the like. When an object is positioned in a movable area, anexpression of “moving conditions are satisfied” will be used below.

If, as a result of the determination by the determination part 43, anyof the conditions is not satisfied (No at step S205 or No at step S206),the process returns to step S202. In contrast, if, as a result of thedetermination by the determination part 43, the position and internalstate of the object O_(k) after the operation u_(kc) at step S204satisfy all conditions (Yes at step S205 and Yes at step S206), the timeis put forward by Δt (t←t+Δt) and the position after the operation atstep S204 is set as (x_(k)(t), y_(k)(t)) and the internal state as(v_(k)(t), θ_(k) (t)) (step S207).

Processing at steps S202 to S207 described above is repeatedly performeduntil the trajectory generation time T passes. That is, if the time tnewly generated at step S207 has not reached T (No at step S208),processing is repeated by returning to step S203. If, on the other hand,the time t newly generated at step S207 reaches T (Yes at step S208), atrajectory for the object O_(k) is output and stored in the storagesection 7 (step S209).

FIG. 4 is a diagram schematically showing a trajectory of the objectO_(k) generated by repeating a set of processing ranging from step S203to step S207 at times t=0, Δt, 2Δt, . . . , T. A trajectory P_(k)(m)(1≦m≦N_(k), m is a natural number) shown in the figure passes through athree-dimensional space-time (x, y, t) of two dimensions (x, y) of spaceand one dimension (t) of time. By projecting the P_(k)(m) onto an x-yplane, a predicted path of the object O_(k) in the two-dimensional space(x, y) can be obtained.

If, after step S209, the value of the counter n_(k) has not reachedN_(k) (No at step S210), the value of the counter n_(k) is incrementedby 1 (step S211) and processing at steps S202 to S207 is performedrepeatedly by returning to step S202 until the trajectory generationtime T has reached.

If the counter n_(k) reaches N_(k) at step S210 (Yes at step S210),generation of the entire trajectories for the object O_(k) is completed.FIG. 5 is an explanatory view schematically showing a set oftrajectories {P_(k)(n_(k))} consisting of N_(k) trajectories P_(k)(1),P_(k)(2), . . . , P_(k)(N_(k)) generated for one object O_(k) in thethree-dimensional space-time. The starting point of each trajectoryconstituting an element of the set of trajectories {P_(k)(n_(k))}, thatis, the initial position (x_(k0), y_(k0), t) is the same (Refer to stepS202). Incidentally, FIG. 5 is strictly a schematic diagram and thevalue of N_(k) can take such values as several hundred to several tensof thousand.

If the counter n_(k) reaches N_(k) at step S210 and the counter k forobject identification has not reached the total number K of the objects(No at step S212), the value of the counter k is incremented by 1 andthe value of the counter n_(k) of the number of times of trajectorygeneration is initialized to 1 (step S213) before returning to step S202to repeat the processing. In contrast, if the counter k of the objectsreaches K (Yes at step S212), trajectory generation for all objects hasbeen completed and the trajectory generation processing at step S2 isterminated before proceeding to step S3 that follows.

By performing trajectory generation processing for a predeterminednumber of times for all objects detected by the sensor section 3, asdescribed above, a space-time environment consisting of a set oftrajectories that could be followed by a plurality of objects presentwithin a predetermined range of the three-dimensional space-time isformed. FIG. 6 is an explanatory diagram schematically showing aconfiguration example of a space-time environment. A space-timeenvironment Env(P₁, P₂) shown in the figure consists of a set oftrajectories {P₁(n₁)} of an object O₁ (indicated by solid lines in FIG.6) and a set of trajectories {P₂(n₂)} of an object O₂ (indicated bysolid lines in FIG. 6). More specifically, the space-time environmentEnv(P₁, P₂) represents a space-time environment when the two objects O₁and O₂ move on a flat and linear road R such as an expressway in the +yaxis direction. Since, in the first embodiment, trajectories aregenerated independently for each object without consideration ofcorrelations between objects, trajectories of different objects maycross in the space-time.

The density per unit volume of the set of trajectories {P_(k)(n_(k))}(k=1, 2) in each area of space-time in FIG. 6 gives a density of theprobability of presence of the object O_(k) in each area of thespace-time (hereinafter, referred to as a “space-time probabilitydensity”). Therefore, by using the space-time environment Env(P₁, P₂)constructed by the trajectory generation processing at step S2, aprobability of the object O_(k) passing through a predetermined area inthe three-dimensional space-time can be determined. Since the abovespace-time probability density is strictly a concept of probability in aspace-time, summation of values thereof in the space-time with respectto one object may not be 1.

If the trajectory generation time T should be set as a fixed value inadvance, a concrete value thereof is preferably a value such that iftrajectories are generated exceeding the time T, the distribution ofprobability density in the space-time will be uniform so that it ismeaningless to calculate. If, for example, the object is a four-wheelvehicle and the four-wheel vehicle travels normally, T may be set atmost to 5 (sec) or so. In this case, if the operation time Δt step S204is about 0.1 to 0.5 (sec), a set of processing from step S203 to stepS207 to generate one trajectory P_(k)(m) will be repeated 10 to 50times.

Incidentally, it is preferable to switch the time T, after setting thetrajectory generation time T for different roads such as an expressway,an ordinary road, and a two-lane road, based on a method by which thetype of road currently running is read from map data using position dataor a method by which the type of road is read by a road recognitionapparatus applying image recognition or the like.

It is also preferable to perform adaptive control in which, afterstatistically evaluating the distribution of probability density in thespace-time using trajectories calculated up to the trajectory generationtime T, if the distribution is uniform, the trajectory generation time Tis reduced and, if the distribution is not uniform, the generation timeis increased.

Further, it is also possible to make a prediction by preparing aplurality of paths that can be taken by subject vehicle in advance andusing a time when the probability that the path of subject vehiclecrosses that of another object becomes constant as the trajectorygeneration time T. In this case, a time when an increment of risk foreach path that can be taken by subject vehicle after increasing theprediction time only by Δt becomes constant may also be adopted as atermination condition. In such a configuration, endpoints on the futureside of the paths that can be taken by subject vehicle are naturally setto be spatially widely distributed to obtain a basis for determinationof paths to be currently taken to secure safety.

After the trajectory generation processing for each object describedabove, the prediction section 5 makes probabilistic predictions of pathsthat can be taken by each object (step S3). A probability of a specifictrajectory P_(k)(m) being selected from among a set of trajectories{P_(k)(n_(k))} generated for the object O_(k) is described below asconcrete prediction calculation processing by the prediction calculationpart 51 in the prediction section 5, but naturally this predictioncalculation is only an example.

When N_(k) trajectories of the object O_(k) are generated, theprobability that one trajectory P_(k)(m) of N_(k) trajectories becomesan actual trajectory is calculated as shown below. First, if theoperation sequence {u_(km)(t)} to realize the trajectory P_(k)(m) of theobject O_(k) is {u_(km)(0), u_(km)(Δt), u_(km)(2Δt), . . . u_(km)(T)},the probability of the operation u_(km)(t) being selected at time t isp(u_(km)(t)) and thus, the probability of the operation sequence{u_(km)(t)} being performed at time t=0 to T is given by:

$\begin{matrix}\left\lbrack {{Formula}\mspace{14mu} 1} \right\rbrack & \; \\{{{{p\left( {u_{km}(0)} \right)} \cdot {p\left( {u_{km}\left( {\Delta \; t} \right)} \right)} \cdot {p\left( {u_{km}\left( {2\; \Delta \; t} \right)} \right)}}\mspace{14mu} \ldots \mspace{14mu} {p\left( {u_{km}(T)} \right)}} = {\prod\limits_{t = 0}^{T}\; {p\left( {u_{km}(t)} \right)}}} & (1)\end{matrix}$

Therefore, when a set of N_(k) trajectories {P_(k)(n_(k))} is given tothe object O_(k), the probability p(P_(k) (m)) of one trajectoryP_(k)(m) that can be followed by the object O_(k) being selected isgiven by:

$\begin{matrix}\left\lbrack {{Formula}\mspace{14mu} 2} \right\rbrack & \; \\{{p\left( {P_{k}(m)} \right)} = \frac{\prod\limits_{t = 0}^{T}\; {p\left( {u_{km}(t)} \right)}}{\sum\limits_{n = 1}^{N_{k}}\left( {\prod\limits_{t = 0}^{T}\; {p\left( {u_{kn}(t)} \right)}} \right)}} & (2)\end{matrix}$

If all operations u_(km)(t) are selected with an equal probability p₀(where 0<p₀<1), Formula (1) is simplified to:

$\begin{matrix}\left\lbrack {{Formula}\mspace{14mu} 3} \right\rbrack & \; \\{{\prod\limits_{t = 0}^{T}\; {p\left( {u_{km}(t)} \right)}} = p_{o}^{s}} & (3)\end{matrix}$

Here, s is the total number of operation times Δt from t=0 to T, thatis, the number of times of operation. Therefore, summation ofprobabilities of the trajectory P_(k)(m) included in N_(k) trajectoriesthat can be followed by the object O_(k) becomes N_(k)p₀ ^(s) and theprobability p(P_(k)(m)) of one trajectory P_(k)(m) among them beingselected is obtained by substituting Formula (3) into Formula (2)

$\begin{matrix}\left\lbrack {{Formula}\mspace{14mu} 4} \right\rbrack & \; \\{{p\left( {P_{k}(m)} \right)} = \frac{1}{N_{k}}} & (4)\end{matrix}$

That is, the probability p(P_(k)(m)) does not depend on the trajectoryP_(k)(m).

If, in Formula (4), the number of trajectories to be generated is thesame (N) for all objects, it follows from N₁=N₂= . . . =N_(k)=N(constant) that p(P_(k)(m))=1/N, which shows that the probability isconstant independent of the object O_(k). By normalizing the value ofthe probability p(P_(k)(m)) to 1 in this case, prediction calculationsby the prediction calculation part 51 can be simplified, leading tofaster execution of predetermined prediction calculations. Incidentally,the probability p(u_(km)(t)) that the operation u_(km)(t) is selectedmay be made to be appropriately settable or changeable by input from theinput section 2.

Based on the probability p(P_(k) (m)) calculated for each object O_(k)(k=1, 2, . . . , K), the prediction calculation part 51 determines aprobability of presence of the object O_(k) per unit volume in each areaof the three-dimensional space-time. The probability of presencecorresponds to the space-time probability density in thethree-dimensional space-time of a set of trajectories {P_(k)(n_(k))} andan area where the density of passing trajectories is high generally hasa higher probability of presence.

After operations by the prediction calculation part 51 described so far,the image generation part 52 generates image information concerning animage to be displayed by the display part 61 of the output section 6 inaccordance with obtained operation results before sending out the imageinformation to the output section 6.

Subsequent to step S3 described above, information in accordance withoperation results by the prediction calculation part 51, that is,prediction results are displayed/output (step S4). FIG. 7 is a diagramshowing a display/output example of prediction results by the displaypart 61 and is a diagram schematically showing a display/output exampleof prediction results when predictions are made by using the space-timeenvironment Env(P₁, P₂) (See FIG. 6) constituted by two objects O₁(subject vehicle) and O₂. More specifically, FIG. 7 shows a case when anarea in which the probability of presence of the other object O₂ after apredetermined time exceeds a predetermined threshold is displayed bytranslucent superimposition on a front glass F of the object O₁ (subjectvehicle). An area D_(a) and an area D_(b) displayed translucently havedifferent illumination (The area D_(a) is brighter). Such a differencein illumination reflects prediction results by the predictioncalculation part 51 and a translucent area having different illuminationis displayed by superimposition on the front glass F in accordance withthe value of the determined probability of presence.

The above display by superimposition is realized by projecting an imagegenerated by the image generation part 52 from a projector (which ispart of the output section 6 and is not shown) set up in an upper partbehind a driver's seat of the object O₁ onto the front glass F.Accordingly, the driver of the object O₁ can immediately recognize anarea where possible danger lurks in the near future while drivingviewing in the forward direction of subject vehicle. Therefore, dangercan precisely be avoided by causing the recognition results to bereflected in driving.

However, the display/output example by the output section 6 is notlimited to this and, for example, prediction results by the predictionsection 5 may be displayed by causing a display screen CN (See FIG. 7)of a car navigation system to have the function of the display part 61.In this case, as same in the areas D_(a) and D_(b) shown in FIG. 8, eacharea on a two-dimensional plane displayed on the display screen CN canbe displayed with gradations in color. Or, information, warning beeps,or voice in accordance with surrounding road situations may be output bycausing a voice to be generated via a microphone from the output section6.

According to the first embodiment of the present invention describedabove, a computer having a storage unit that stores the position of anobject and an internal state including the speed of the object reads theposition and internal state of the object from the storage unit,generates trajectories in a space-time consisting of time and space fromchanges of the positions that can be taken by the object with thepassage of time based on the read position and internal state of theobject, and probabilistically predicts the path of the object by usingthe generated trajectory so that safety can be secured even insituations that can actually occur.

Also, according to the first embodiment, by making path predictions ofobjects using a space-time environment formed in a space-time consistingof time and space, path predictions of not only static objects, but alsodynamic objects can be made with precision.

Further, according to the first embodiment, since trajectories ofdetected objects are generated independently, a specific object (forexample, subject vehicle) can be distinguished from other objects. As aresult, danger that may lurk between the specific object and the otherobjects can be predicted easily and precisely within a practical periodof time.

In addition, according to the first embodiment, since informationincluding danger can be presented by outputting results predicted byusing a space-time environment, a driver of subject vehicle can drivewhile swiftly and preciselyavoiding danger that could lurk in the nearfuture during driving.

Incidentally, the first embodiment is also applicable, as describedabove, in the four-dimensional space-time (three dimensions for spaceand one dimension for time). The first embodiment is naturallyapplicable to an automobile running on a road with a difference ofelevation and, in addition, when a movable body moving in the air likean airplane or helicopter makes path predictions of other movable bodiesmoving similarly in the air.

Here, a difference between Nonpatent Literature 1 cited in the abovebackground art and the first embodiment will be described. While thesetwo technologies both make path predictions of objects using the conceptof probability, the technology in Nonpatent Literature 1 does notpredict paths of objects within a predetermined range independently andmakes only probability calculations based on mutual correlations. Thus,when any two objects of a plurality of objects collide, path predictionsof the two objects end when the two objects collide. When considered inthe three-dimensional space-time, this means that collisiondetermination processing after trajectories of two different objectscross will not be performed.

In contrast, in the first embodiment, an object trajectory is generatedindependently for each object and therefore, collision determinationprocessing continues until a predetermined time elapses even iftrajectories of different objects cross in the three-dimensionalspace-time. Thus, a space-time environment generated according toNonpatent Literature 1 and that generated according to the firstembodiment are quite different qualitatively. In addition, since pathfinding is performed for each object independently without consideringobject correlations in the first embodiment, the calculated amount issmaller than in Nonpatent Literature 1.

In addition, according to Nonpatent Literature 1, even if an event ofcollision can be predicted, when such a collision occur cannot bepredicted. This is because the technology in Nonpatent Literature 1focuses on searching for presence/absence of collision for each state ateach time, instead of determining probabilities that objects collide inthe flow of time. In other words, a space-time environment is not usedexplicitly in Nonpatent Literature 1 and also, the concept of space-timeprobability density is not employed.

Though the first embodiment and Nonpatent Literature 1 may at firstglance give an impression of being similar technologies because bothmake path predictions using the concept of probability, technologicalprinciples thereof are essentially quite different and it is extremelydifficult even for a person skilled in the art to reach the firstembodiment from Nonpatent Literature 1.

Modification of the First Embodiment

The operation selection part 41 of the trajectory generation section 4may maintain an operation at the present time only for subject vehicle.In this case, the internal state at prediction times of subject vehicleis maintained and a sole operation will continue to be performed andtherefore, the operation selection probability of selecting theoperation is 1 and only one trajectory is generated in a space-time as aset of trajectories of subject vehicle.

FIG. 9 shows a space-time environment generated when an operation ofsubject vehicle is maintained as described above, and is a diagramcorresponding to FIG. 6. In a space-time environment Env′ (P₁, P₂) shownin FIG. 9, the set of trajectories of the object O₁ (subject vehicle) inthe three-dimensional space-time consists of only one linear trajectory(similar to FIG. 6 for the object O₂). By applying a model in which anoperation of subject vehicle O₁ is maintained, as described above,situations can be simplified for prediction when, for example, there aremany surrounding objects, leading to reduced calculated amount in thetrajectory generation section and the prediction section.

Second Embodiment

A second embodiment of the present invention is characterized in thatwhen trajectories are generated for each object, all selectableoperations are performed for trajectory generation. The functionalconfiguration of an object path prediction apparatus according to thesecond embodiment is the same as that of the object path predictionapparatus 1 according to the first embodiment (See FIG. 1). An objectpath prediction method according to the second embodiment is the same asthat according to the first embodiment except trajectory generationprocessing for each object.

FIG. 10 is a flowchart showing the overview of trajectory generationprocessing (corresponding to step S2 in FIG. 2) by the object pathprediction method according to the second embodiment. In the trajectorygeneration processing shown in the figure, firstly, initialization toset the value of the counter k for identifying each object to 1 isperformed (step S21). Also in the second embodiment, the total number ofthe objects for which trajectories should be generated is assumed to beK.

Next, the trajectory generation section 4 reads results detected by thesensor section 3 from the storage section 7 and sets the read detectionresults as an initial state (step S22). More specifically, the time t isset to 0 and the initial position (x_(k)(0), y_(k)(0)) and the initialinternal state (v_(k)(0), θ_(k)(0)) are set to input information(x_(k0), y_(k0)) and (v_(k0), θ_(k0)) from the sensor section 3respectively.

Subsequently, trajectories of the object O_(k) in the three-dimensionalspace-time (x, y, t) are generated (step S23). FIG. 11 is a flowchartshowing details of trajectory generation processing at step S23. In adescription that follows, the trajectory generation time T is assumed tobe represented by T=JΔt (J is a natural number) using the operation timeΔt which each operation is performed.

First, loop processing (Loop1) at time t=0 is started (step S231-1). Inthis Loop1, an operation u_(k)(0) at t=0 is formed for Δt. Concretecontent of the operation u_(k)(0) is determined, as same in the firstembodiment, in accordance with the type of object O_(k) (If the objectis a vehicle, the operation can be specified by the acceleration orangular velocity, and if the object is a person, the operation can bespecified by the speed). A set of operations {u_(kc)} consists of finiteelements and if a selectable operation is a continuous quantity,elements of the set {u_(kc)} are constituted by discretization ofappropriate intervals.

More specific processing at step S231-1 will be described. Firstly, theoperation selection part 41 selects one operation u_(kc)(0) and theobject operation part 42 causes the selected operation u_(kc)(0) to beperformed for Δt. After the operation, the determination part 43determines whether or not the position and internal state of the objectO_(k) satisfy control conditions and movement conditions similar tothose in the first embodiment. If all conditions are satisfied (OK), thedetermination part 43 proceeds to loop processing (Loop2) at the nexttime t=Δt. In contrast, if any of the control conditions or movementconditions is not satisfied (NG), the operation u_(kc)(0) performedimmediately before is canceled after proceeding to step S233-1. Sincethe operation selection part 41 selects all operations in the secondembodiment, the order of selecting each operation is arbitrary. Thisapplies also to subsequent loop processing (Loop2, Loop3, . . . ,LoopJ).

Processing up to generation of one trajectory will first be describedbelow. In Loop2, as same in the above Loop1, the operation selectionpart 41 selects an operation and the operation u_(kc)(Δt) is performedonly for Δt. Then, if the position of the object O_(k) after theoperation satisfies control conditions and movement condition similar tothose above (OK), the processing proceeds to loop processing (Loop3) attime 2Δt. If, on the other hand, any of the control conditions ormovement conditions is not satisfied (NG), the operation u_(kc)(Δt)performed immediately before is canceled after proceeding to stepS233-2.

Hereafter, by repeating processing similar to that in the above Loop1 orLoop2, loop processing is continuously performed J times. That is, aslong as the object O_(k) after an operation being performed at times3Δt, 4Δt, . . . satisfies control conditions and movement conditions,the processing proceeds to Loop4, Loop5, . . . , sequentially. Then, ifthe object O_(k) satisfies control conditions and movement conditions upto the last LoopJ, the processing proceeds to the subsequent step S232.At step S232, one trajectory from t=0 to t=T (=JΔt) is output and storedin the storage section 7. The trajectory passes through thethree-dimensional space-time as in the trajectory P_(k)(m) shown in FIG.4.

At step S233-J after step S232, the operationu_(kc)(T−Δt)=u_(kc)((J−1)Δt) performed at the latest time is canceledand, if LoopJ is to be continued (LoopJ continuation), the processingreturns to step S231-J. If, on the other hand, LoopJ is to be terminated(LoopJ termination), the processing proceeds to the subsequent stepS233-(J−1).

At step S233-(J−1), the operation u_(kx)(T−2Δt) performed in Loop(J−1)is canceled and, if Loop(J−1) is to be continued (Loop(J−1)continuation), that is, if any element to be performed as the operationu_(kc)(T−2Δt) remains, the processing returns to step S231-(J−1) torepeat processing. If, on the other hand, Loop(J−1) is to be terminated(Loop(J−1) termination), that is, no element to be performed as theoperation u_(kc)(T−2Δt) remains, the processing proceeds to thesubsequent step S233-(J−2).

Hereafter, processing similar to that in the above LoopJ or Loop(J−1) isrepeated in the order of Loop(J−2), . . . , Loop2, Loop1. As a result,when proceeding to processing at step S24 after completing Loop1 at stepS233-1 lastly, all possible trajectories that can be followed by theobject O_(k) haven been generated, that is, set of trajectories {Pk(nk)}is generated as shown in FIG. 5.

Next, a case when any one of control conditions or movement conditionsis not satisfied (NG) at step S231-1 will be described. In this case,the operation u_(kc)(0) performed immediately before is canceled afterproceeding to step S233-1. Then, if Loop1 is to be continued, theprocessing returns to step S231-1 and, if Loop1 is to be terminated, theprocessing proceeds to the subsequent step S24.

If, after causing an operation selected at step S231-2, S231-3, . . . ,or 231-J to perform, the object O_(k) does not satisfy any of controlconditions or movement conditions, processing similar to that of theabove step S231-1 is performed. That is, generally if the object O_(k)does not satisfy any of control conditions or movement conditions atstep S231-j (j=2, 3, . . . , J), the operation performed immediatelybefore can be canceled after proceeding to step S233-j. Accordingly, ifany condition is not satisfied at some time t_(k), trajectory generationprocessing after t_(k) can be omitted, realizing reduction in calculatedamount.

The algorithm of trajectory generation processing described above isequal to that used for searching for all possible operations by using arecursion based on the depth-first search. Therefore, in this case, thenumber of elements of a set of trajectories {P_(k)(n_(k))} generated inthe end for one object O_(k), that is, the number of trajectories is notknown until trajectory generation processing for the object O_(k) iscompleted.

Instead of performing the above trajectory generation processing, thebreadth-first search may be used to search for all possible operations.To generate trajectories that can be followed by each object bysearching for all practicable operations, a search method having anoptimal calculated amount in accordance with the number of elements ofthe operation u_(kc)(t) (the level of discretization when the operationu_(kc)(t) is a continuous quantity) in the operation time Δt may beselected.

If, after the trajectory generation processing at step S23 describedabove, the counter k for object identification has not reached K (No atstep S24), the value of the counter is incremented by 1 (step S25),initialization based on detection results by the sensor section 3 isperformed after returning to step S22, and the above trajectorygeneration processing (Step S23) is performed for another objectO_(k+1). If, on the other hand, the counter k for object identificationreaches K (Yes at step S24), trajectory generation processing for allobjects present within a predetermined range has been completed andthus, the trajectory generation processing (corresponding to step S2 inFIG. 2) is terminated. As a result, a space-time environment Env(P₁, P₂)similar to that shown in FIG. 6 is generated and stored in the storagesection 7.

Processing hereafter, that is, probabilistic predictions of object pathsby the prediction section 5 (corresponding to step S3 in FIG. 2) andoutput of prediction results by the output section 6 (corresponding tostep S4 in FIG. 2) are similar to those in the first embodiment.

According to the second embodiment of the present invention describedabove, a computer having a storage unit that stores the position of anobject and an internal state including the speed of the object reads theposition and internal state of the object from the storage unit,generates trajectories in a space-time consisting of time and space fromchanges of the positions that can be taken by the object with thepassage of time based on the read position and internal state of theobject, and probabilistically predicts the path of the object by usingthe generated trajectory so that safety can be secured even insituations that can actually occur.

Also, according to the second embodiment, by making path predictions ofobjects using a space-time environment formed in a space-time consistingof time and space, path predictions of not only static objects, but alsodynamic objects can be made with precision.

Incidentally, trajectory generation processing in a space-time using arecursion is performed in the second embodiment, but it is impossible tosay with absolute certainty, when compared with the trajectorygeneration processing in the first embodiment described above, whichtrajectory generation processing requires less calculated amount. Inother words, which algorithm to use to generate a trajectory of a bodyin a space-time changes depending on various conditions including theoperation time Δt, the trajectory generation time T, and the number ofelements in a set of operations. Thus, the most optimal algorithm can beadopted in accordance with conditions under which predictions are made.

Third Embodiment

FIG. 12 is a block diagram showing the functional configuration of anobject path prediction apparatus according to a third embodiment of thepresent invention. An object path prediction apparatus 101 shown in thefigure is an apparatus mounted on a movable body such as a four-wheelvehicle to detect objects present within a predetermined range aroundsubject vehicle, which is a specific object, to predict paths ofdetected objects and subject vehicle, and based on results of theprediction, to quantitatively evaluate the level of interference betweenpaths that can be taken by the specific object, subject vehicle, andthose that can be taken by other objects.

The object path prediction apparatus 101 includes the input section 2into which various kinds of information are input from outside, thesensor section 3 for detecting positions and internal states of objectspresent within a predetermined range, the trajectory generation section4 for generating a trajectory in space-time constituted by time andspace from changes of the positions that can be taken by an object withthe passage of time based on results detected by the sensor section 3, aprediction section 105 for making a probabilistic prediction about thepath of the object using the trajectory generated by the trajectorygeneration section 4, an interference level calculation part 106 forcalculating the level of interference showing quantitatively an extentof interference between paths that can be taken by subject vehicle andthose that can be taken by other objects based on results of predictionsmade by the prediction section 105, an output section 107 for outputtingvarious kinds of information including evaluation results by theinterference level calculation part 106, and a storage section 108 forstoring various kinds of information including positions and internalstates of objects detected by the sensor section 3. In FIG. 12, the samereference numerals are granted to components having the same functionalconfiguration as that of the object path prediction apparatus 1according to the first embodiment as in FIG. 1.

The output section 107 has a display part 171 for displaying/outputtingan image in accordance with evaluation results by the interference levelcalculation part 106 and a warning beep generation part 172 forgenerating a warning beep in according with evaluation results thereof.The warning beep generation part 172 is realized by using a speaker orthe like.

The storage section 108 stores, in addition to detection results by thesensor section 3, trajectories generated by the trajectory generationsection 4, results of predictions made by the prediction section 105,results of interference level calculation by the interference levelcalculation part 106, operations selected by the operation selectionpart 41 in the trajectory generation section 4 and the like.

Next, an object path prediction method according to the third embodimentof the present invention will be described. FIG. 13 is a flowchartshowing a process overview of the object path prediction methodaccording to the third embodiment. Also in the third embodiment, allobjects to be predicted are assumed to move on a two-dimensional planefor a description.

In the third embodiment, detection processing of the position andinternal state of each object (step S31), trajectory generationprocessing for each object in a space-time (step S32), and probabilisticprediction processing of object paths using trajectories (step S33) arethe same as step S1, step S2, and step S3 described above respectively.In a description that follows, it is assumed that the trajectorygeneration processing at step S32 is performed by the method based onthe operation selection probability described in the first embodiment,but it is also possible to adopt the method described in the secondembodiment, that is, the method by which trajectories are generated byperforming all selectable operations.

Incidentally, when performing trajectory generation processing for eachobject in a space-time at step S32, it is important to terminateprediction calculations based on the trajectory generation time T,instead of whether subject vehicle has arrived at a preset location (adestination or an intermediate location analogous to a destination), interms of technological principles. There is no place on an ordinary roadwhere safety is secured in advance. For example, as shown in FIG. 14,when making a prediction by assuming that subject vehicle O₁ running ona three-lane road Rd sequentially reaches preset locations Q₁, Q₂, andQ₃, considering a case in which subject vehicle O₁ travels almost in astraight line toward the preset locations, there is a danger thatanother vehicle O₂ takes a path B₂ to move into the lane on whichsubject vehicle O₁ is running to avert danger caused by another vehicleO₃ taking a path B₃. Accordingly, conventional path predictioncalculations do not guarantee in advance to the extent that it is safefor subject vehicle O₁ to travel to a preset location.

Since, in the third embodiment, an optimal path is determined each timewithout presetting a location such as a destination to be reached bysubject vehicle O₁, for example, a path B₁ shown in FIG. 15 can beselected as a path of subject vehicle O₁ under the same situation asthat in FIG. 14 so that safety can be secured by precisely avertingdanger when subject vehicle O₁ travels.

Instead of the trajectory generation time T, the condition forterminating prediction calculation may be determined by a trajectorygeneration length showing the length of a trajectory to be generated. Inthis case, it is preferable to adaptively change the trajectorygeneration length depending on the speed of an object (or the speed ofsubject vehicle).

Processing at step S34 and thereafter will be described below in detail.At step S34, the level of interference between subject vehicle andanother vehicle is calculated by the interference level calculation part106 (step S34). FIG. 16 is a flowchart showing details of interferencelevel calculation processing. In the third embodiment, the object O₁ isassumed to be subject vehicle. For convenience of description, otherobjects O_(k) (k=2, 3, . . . , K) are all assumed to be also four-wheelvehicles and called the other vehicle O_(k). Interference levelcalculation processing shown in FIG. 16 consists of processing of fourloops and calculates individually the levels of interference of allelements of a set of trajectories {P₁(n₁)} of subject vehicle O₁determined at step S33 with all sets of trajectories {P_(k)(n_(k))} ofthe other vehicle O_(k).

Input received by the interference level calculation part 106 at stepS34 includes the set of trajectories {P₁(n₁)} of subject vehicle O₁, allsets of trajectories {P_(k)(n_(k))} of the other vehicle O_(k), and aninterference level evaluation function to evaluate the level ofinterference between subject vehicle O₁ and the other vehicle O_(k). Itis assumed in the third embodiment that the interference levelcalculation part 106 contains the interference level evaluationfunction, but the interference level evaluation function may be inputfrom outside. Or, the interference level evaluation function may becaused to adaptively change depending on the type of road and the speedof subject vehicle O₁.

By evaluating the levels of interference between sets of trajectories ofother vehicles and a set of trajectories of subject vehicle withendpoints different from each other, as described using FIG. 15, anoptimal path is determined each time without presetting a location suchas a destination to be reached by subject vehicle to precisely avertdanger while subject vehicle travels so that safety can be secured. As aresult, as shown in FIG. 14, a fatal problem that safety is not securedeven if subject vehicle travels toward a preset location on a road canbe solved.

In FIG. 16, the interference level calculation part 106 first startsrepetitive processing (Loop1) for all trajectories of subject vehicleO₁(step S401). For this purpose, one trajectory is selected from the setof trajectories {P₁(n₁)} and subsequent processing is performed for theselected trajectory.

Next, the interference level calculation part 106 starts repetitiveprocessing (Loop2) for the other vehicle O_(k)(step S402). In thisLoop2, the counter k for identification of other vehicles is initializedto k=2 and the value of k is incremented each time repetitive processingis completed.

Inside Loop2, repetitive processing (Loop3) for all elements of the setof trajectories {P_(k)(n_(k))} generated at step S3 for the othervehicle O_(k) is performed (step S403). In this repetitive processing,the level of interference determined by a counter n₁ for repetition ofLoop1, that is, for identifying a trajectory generated for subjectvehicle O₁ and the counter k for identification of other vehicles is setas r₁(n₁, k) and the value of r₁(n₁, k) is set to 0 (step S404).

Subsequently, the interference level calculation part 106 startsrepetitive processing (Loop4) to evaluate interference between thetrajectory P₁(n₁) of subject vehicle O₁ and the trajectory P_(k)(n_(k))of the other vehicle O_(k)(step S405). In this Loop4, the distancebetween the trajectory P₁(n₁) and the trajectory P_(k)(n_(k)) at thesame time is sequentially determined at times t=0, Δt, . . . , T. Sincethe position of each trajectory in the two-dimensional space-time isdefined as the center of each vehicle, if a spatial distance between twotrajectories becomes smaller than a predetermined value (for example,the standard width or length of a vehicle), it is possible to assumethat subject vehicle and the other vehicle O_(k) have collided. In thissense, two objects may be determined to have collided even if coordinatevalues of the two vehicles do not match. Hereinafter, the maximal value(spatial distance at which two vehicles interfere with each other) ofdistance that allows considering that two objects have collided will becalled an interference distance.

FIG. 17 is a diagram schematically showing a relationship between thetrajectory P₁(n₁) of subject vehicle O₁ and the trajectory P_(k)(n_(k))of the other vehicle O_(k) in a space-time. In the case shown in thefigure, the trajectory P₁(n₁) and the trajectory P_(k)(n_(k)) cross attwo points C₁ and C₂. Therefore, there exist areas A₁ and A₂ where thedistance between the two trajectories at the same time is smaller thanthe interference distance near these two points C₁ and C₂. That is, adetermination is made that subject vehicle O₁ and the other vehicleO_(k) collide at a time when the trajectory P₁(n₁) and trajectoryP_(k)(n_(k)) are contained in the areas A₁ and A₂ respectively. In otherwords, the number of times of passing through the areas A₁ and A₂ attimes t=0, Δt, . . . , T is the number of times of collision betweensubject vehicle O₁ and the other vehicle O_(k).

As is evident from FIG. 17, in a space-time environment formed in thethird embodiment, even if two trajectories collide, trajectoriesthereafter are generated. This is because trajectories are generated foreach object independently.

If, as a result of determining the distance between subject vehicle O₁and the other vehicle O_(k), a determination is made that subjectvehicle O₁ and the other vehicle O_(k) have collided in the sensedescribed above (Yes at step S406), the interference level calculationpart 106 sets the value of the level of interference r₁(n₁, k) as

[Formula 5]

r ₁(n ₁ ,k)←r ₁ ,k)+c _(1k) ·p(P _(k)(n _(k)))·F(t)  (5)

(step S407). Here, the second term c_(1k)·p(P_(k) (n_(k)))·F(t) will bedescribed. The coefficient c_(1k) is a positive constant and can be set,for example, as c_(1k)=1. p(P_(k)(n_(k))) is a quantity defined byFormula (2) and is a probability that one trajectory P_(k)(n_(k)) isselected for the other vehicle O_(k). The last term F(t) is a quantitythat gives time dependence of interference between objects in onecollision. Therefore, if no time dependence of interference betweenobjects is not allowed, the value of F(t) can be set to be constant. Incontrast, if time dependence of interference between objects is allowed,as shown, for example, in FIG. 18, F(t) may be defined as a functionwhose value decreases with the passage of time. F(t) shown in FIG. 18 isapplied when importance is granted to the latest collision.

If, after step S407, the time t has not reached T, the interferencelevel calculation part 106 repeats Loop4 (No at step S408). In thiscase, the value of t is incremented by Δt (step S409) and repeats Loop4after returning to step S405. If, on the other hand, after step S407,the time t reaches T, Loop4 is terminated (Yes at step S408). If subjectvehicle O₁ and the other vehicle O_(k) do not collide at some time t,the p interference level calculation part 106 directly proceeds todetermination processing (step S408) whether to repeat Loop4.

By the repetitive processing of Loop4 described above, the value of thelevel of interference r₁(n₁, k) increases as the number of times ofcollision increases. After Loop4 is completed, determination processingwhether to repeat Loop3 is performed at step S410. That is, if there isany of trajectories generated for the other vehicle O_(k) whoseinterference evaluation with one trajectory P₁(n₁) of subject vehicle O₁has not been performed (No at step S410), n_(k) is incremented ton_(k)+1 (step S411) and Loop3 is repeated after returning to step S403.

In contrast, if all interference evaluations of trajectories generatedfor the other vehicle O_(k) with one trajectory P₁(n₁) of subjectvehicle O₁ have been performed (Yes at step S410), the final level ofinterference r₁(n₁, k) that evaluates interference between thetrajectory P₁(n₁) of subject vehicle O₁ and all trajectories of theother vehicle O_(k) is attached (step S412) and the attached value isoutput and stored in the storage section 108 (step S413).

The value of the final level of interference r₁(n₁, k) output from theinterference level calculation part 106 at step S413 depends on theprobability p(P_(k)(n_(k))) that one trajectory P_(k)(n_(k)) is selectedfrom among all trajectories of the other vehicle O_(k). Thus, if it isassumed in Formula (5) that the coefficient c_(1k) is not dependent on kand is constant (for example, c_(1k)=1), F(t) is constant (for example,1), and the number of times of collision between the trajectory P₁(n₁)of subject vehicle O₁ and the trajectory P_(k)(n_(k)) of the othervehicle O_(k) is M_(1k)(n₁, n_(k)), the value of the level ofinterference r₁(n₁, k) is obtained by multiplying the probabilityp(P_(k) (n_(k))) for each trajectory P_(k)(n_(k)) by M_(1k)(n₁, n_(k))and adding up for elements of all sets of trajectories {P_(k)(n_(k))}.The sum is none other than the collision probability that one trajectoryP₁(n₁) of subject vehicle O₁ and trajectories that can be followed bythe other vehicle O_(k) collide. Therefore, the value obtained as thelevel of interference r₁(n₁, k) in the end increases in proportion tothe collision probability that one trajectory P₁(n₁) of subject vehicleO₁ and the other vehicle O_(k) collide.

Subsequent to step S413, the interference level calculation part 106performs determination processing whether to repeat Loop2. If thereremains another vehicle O_(k) whose interference evaluation with subjectvehicle O₁ should be performed (No at step S414), the interference levelcalculation part 106 increments the value of k by 1 (step S415) andrepeats Loop2 after returning to step S402. If, on the other hand, thereremains no vehicle O_(k) whose interference evaluation with subjectvehicle O₁ should be performed (Yes at step S414), the interferencelevel calculation part 106 proceeds to the subsequent step S416.

At step S416, determination processing whether to repeat Loop1 isperformed. More specifically, if there remains a trajectory of the setof trajectories {P₁(n₁)} of subject vehicle O₁ whose interferenceevaluation should be performed (No at step S416), the interference levelcalculation part 106 increments the value of n₁ by 1 (step S417) andrepeats Loop1 after returning to step S401. If, on the other hand, thereremains no trajectory of the set of trajectories {P₁(n₁)} of subjectvehicle O₁ whose interference evaluation should be performed (Yes atstep S416), the interference level calculation part 106 terminates Loop1before terminating interference level calculation processing (step S34).

Then, the output section 107 outputs information in accordance with thelevel of interference calculated at step S34 as evaluation results (stepS35). A case in which the display part 171 in the output section 107performs a translucent display by superimposition as shown inn FIG. 7 onthe front glass F of subject vehicle O₁ will be described below. FIG. 7in this case can be interpreted as a figure showing a situation inwhich, in a space-time environment Env(P₁, P₂) constituted by subjectvehicle O₁ and another vehicle O₂, an area where, among paths that canbe taken by subject vehicle O₁ on a two-dimensional plane in accordancewith the level of interference r₁(n₁, 2) between subject vehicle O₁ andthe other vehicle O₂, the value of the level of interference r₁(n₁, 2)exceeds a preset threshold is displayed by superimposition.

The display part 171 has a function to change illumination in accordancewith the value of the level of interference r₁(n₁, 2). If, for example,a setting is made to increase illumination as the value of the level ofinterference r₁(n₁, 2) increases and, of the area D_(a) and the areaD_(b) shown in FIG. 7, illumination of the area D_(a) is greater. Inthis case, the driver of subject vehicle O₁ can immediately recognizethat, by referencing a display by superimposition on the front glass Fwhile driving viewing in the forward direction, driving to avert dangercan be done by taking a path toward the area D_(b) where the value ofthe level of interference r₁(n₁, 2) is relatively smaller, By causingthis recognition result to be reflected in driving operation, the drivercan precisely avert danger that could occur on subject vehicle O₁ in thenear future. In addition to displaying by the display part 171, thewarning beep generation part 172 may generate a warning beep (includingvoice) when the value of the level of interference r₁(n₁, 2) obtainedfor an anticipated path in accordance with the current operation exceedsa predetermined threshold.

Also, as same in the first embodiment, interference evaluation resultsby the interference level calculation part 106 may be displayed bycausing the display screen CN (See FIG. 8) of a car navigation system tohave the function of the display part 171.

According to the third embodiment of the present invention describedabove, a computer having a storage unit that stores at least thepositions of a plurality of objects and an internal state including thespeed of each object reads the positions and internal states of theplurality of objects from the storage unit, generates trajectories in aspace-time consisting of time and space from changes of the positionsthat can be taken by each of the plurality of objects with the passageof time based on the read positions and internal states of the objects,predicts probabilistically paths of the plurality of objects by usingthe generated trajectory, and based on results of the prediction,calculates the level of interference quantitatively showing an extent ofinterference between trajectories that can be followed by the specificobject in the space-time and those that can be followed by the otherobjects so that safety can be secured even in situations that canactually occur.

Also, according to the third embodiment, by applying the level ofinterference defined by using the collision probability in a space-time,possibilities of collision with other objects can precisely be predictedwithin a practical period of time.

Further, according to the third embodiment, by making path predictionsof objects using a space-time environment formed in a space-timeconsisting of time and space, path predictions of not only staticobjects, but also dynamic objects can be made with precision.

In addition, according to the third embodiment, since trajectories ofdetected objects are generated independently, a specific object (forexample, subject vehicle) can be distinguished from other objects. As aresult, danger that may lurk between the specific object and the otherobjects can be predicted easily and precisely.

Incidentally, the coefficient c_(1k) in Formula (5) for increasing thevalue of the level of interference r₁(n₁, k) may not be constant. Forexample, the coefficient c_(1k) may be a magnitude of relative velocitybetween subject vehicle O₁ and the other vehicle O_(k) at the time ofcollision. Generally, as the magnitude of relative velocity increases,an impact during collision increases. Therefore, if the coefficientc_(1k) is the magnitude of relative velocity between vehicles at thetime of collision, the level of impact of collision between vehicleswill be added to the level of interference r₁(n₁, k).

Or, a value indicating seriousness of damage may be assigned to thecoefficient c_(1k). In this case, for example, the magnitude of relativevelocity between vehicles during collision may be stored in the storagesection 108 by associating with a damage scale evaluation value fornumerically evaluating the damage scale caused by a collision or anamount of damage losses caused by a collision before the stored valueread from the storage section 108 being assigned to the coefficient clk.If the sensor section 3 has a function capable of detecting even thetype of object, the damage scale evaluation value or amount of damagelosses may be determined in accordance with the type of object. In thiscase, for example, when the object against which to collide is a humanor a vehicle, it is preferable to reduce the possibility to collideagainst a human to a minimum by, for example, setting the value of thecoefficient clk for collision against a human far greater than that ofthe coefficient c_(1k) for collision against other objects.

Incidentally, in the third embodiment, the level of interference may becalculated by assuming that an operation of subject vehicle O₁ ismaintained. In that case, the display part 171 in the output section 107can display not only predicted paths of subject vehicle O₁, but alsodanger of other vehicles running within a predetermined range inaccordance with calculation results of the level of interference.

By applying a model in which an operation of subject vehicle O₁ ismaintained, as described above, situations can be simplified forprediction when, for example, there are many surrounding objects,leading to reduced calculated amount in the trajectory generationsection, the prediction section and the interference level calculationpart.

Here, a difference between Nonpatent Literature 1 cited in the abovebackground art and the third embodiment will be described. Since pathfinding is performed for each object independently without consideringobject correlations in the third embodiment, the calculated amount issmaller than in Nonpatent Literature 1. Particularly, the number oftimes of calculating the level of interference per trajectory in thethird embodiment is given by

$\begin{matrix}\left\lbrack {{Formula}\mspace{14mu} 6} \right\rbrack & \; \\{n_{1} \times {\sum\limits_{k = 2}^{K}n_{k}}} & \;\end{matrix}$

and the calculated amount on the order of the square of the number oftrajectories is sufficient regardless of the total number of objectsconstituting a space-time environment. In contrast, when evaluatinginterference according to Nonpatent Literature 1, a specific object(subject vehicle) and other objects (other vehicles) are notdistinguished and the calculated amount for evaluating mutualinterference (corresponding to the number of times of calculating thelevel of interference in the first embodiment) is given by:

$\begin{matrix}\left\lbrack {{Formula}\mspace{14mu} 7} \right\rbrack & \; \\{n_{1} \times {\sum\limits_{k = 2}^{K}n_{k}}} & \;\end{matrix}$

and therefore, the calculated amount on the order of the K-th power ofthe number of trajectories is needed. As a result, as the number ofobjects constituting a space-time environment increases, a difference ofcalculated amount from that in the third embodiment will beconspicuously larger.

Differences between Nonpatent Literature 1 and the third embodimentexcluding the difference described here are the same described for thefirst embodiment. Therefore, as same in the first embodiment, it isextremely difficult even for a person skilled in the art to reach thethird embodiment from Nonpatent Literature 1.

Fourth Embodiment

A fourth embodiment of the present invention is characterized in thatthe level of interference when evaluating interference is defined byusing the minimum collision time between subject vehicle and othervehicles. The functional configuration of an object path predictionapparatus according to the fourth embodiment is the same as that of theobject path prediction apparatus 101 according to the third embodiment(See FIG. 12). An object path prediction method according to the fourthembodiment is the same as that according to the third embodiment exceptinterference level calculation processing.

FIG. 19 is a flowchart showing details of interference level calculationprocessing (corresponding to step S34 in FIG. 13) by the object pathprediction method according to the fourth embodiment. Also in the fourthembodiment, the object O₁ is assumed to be subject vehicle. Forconvenience of description, other objects O_(k) (k=2, 3, . . . , K) areall assumed to be also four-wheel vehicles and called the other vehicleO_(k).

The interference level calculation part 106 first starts repetitiveprocessing (Loop1) for all trajectories of subject vehicle O₁(stepS421). For this purpose, one trajectory is selected from the set oftrajectories {P₁(n₁)} and subsequent processing is performed for theselected trajectory.

Next, the interference level calculation part 106 starts repetitiveprocessing (Loop2) for the other vehicle O_(k)(step S422). In thisLoop2, the counter k for identification of other vehicles is initializedto k=2 and the value of k is incremented each time repetitive processingis completed.

The interference level calculation part 106 performs repetitiveprocessing (Loop3) for all elements of the set of trajectories{P_(k)(n_(k))} generated at step S33 also for the other vehicle O_(k) isperformed (step S423). In this repetitive processing, the level ofinterference determined by the counter n₁ for repetition of Loop1, thatis, for identifying a trajectory generated for subject vehicle O₁ andthe counter N₁ for identification of other vehicles is set as r₁(n₁, k)and the trajectory generation time T is set as the value of r₁(n₁, k)(step S424).

Subsequently, the interference level calculation part 106 startsrepetitive processing (Loop4) to evaluate interference between thetrajectory P₁(n₁) of subject vehicle O₁ and the trajectory P_(k)(n_(k))of the other vehicle O_(k)(step S425). In this Loop4, the distancebetween the trajectory P₁(n₁) and the trajectory P_(k)(n_(k)) at thesame time is sequentially determined at times t=0, Δt, . . . , T todetermine whether subject vehicle O₁ and the other vehicle O_(k) havecollided. The definition of collision for this determination is the sameas that in the third embodiment and a determination is made that acollision has occurred when the distance between subject vehicle O₁ andthe other vehicle O_(k) is shorter than the interference distance.

If, as a result of determining the distance between subject vehicle O₁and the other vehicle O_(k), the interference level calculation part 106determines that subject vehicle O₁ and the other vehicle O_(k) havecollided (Yes at step 426), and the value of the level of interferencer₁(n₁, k) is greater than the time t at that time (the time needed forcollision from the initial position) (Yes at step S427), t is set as thevalue of r₁(n₁, k) and then t is set as T (step S428). Therefore, inthis case, Loop4 terminates (Yes at step S429).

In contrast, if subject vehicle O₁ and the other vehicle O_(k) collide(Yes at step 426) and the value of the level of interference r₁(n₁, k)is equal to or smaller than the time t at that time (No at step S427),the interference level calculation part 106 proceeds to step S429 todetermine whether to terminate Loop4. When subject vehicle O₁ and theother vehicle O_(k) do not collide (No at step 426), the interferencelevel calculation part 106 also proceeds to step S429.

If, at step S429, the time t has not reached T, Loop4 is repeated (No atstep S429). In this case, the interference level calculation part 106increments the value of t by Δt (step S430) and returns to step S425before repeating Loop4. If, on the other hand, the time t has reached Tat step S429, the interference level calculation part 106 terminatesLoop4 (Yes at step S429).

With the repetitive processing of Loop4 described above, the value ofthe level of interference r₁(n₁, k) will be the minimum collision timethat is the shortest time needed for collision from the initial positionamong collisions occurring between subject vehicle O₁ and the othervehicle O_(k).

After terminating Loop4, the interference level calculation part 106performs determination processing whether to repeat Loop3. That is, ifthere is any of trajectories generated for the other vehicle O_(k) whoseinterference evaluation with one trajectory P₁(n₁) of subject vehicle O₁has not been performed (No at step S431), n_(k) is incremented ton_(k)+1 (step S432) and Loop3 is repeated after returning to step S423.

If, on the other hand, all interference evaluations of trajectoriesgenerated for the other vehicle O_(k) with one trajectory P₁(n₁) ofsubject vehicle O₁ have been performed (Yes at step S431), interferenceevaluations with one trajectory P_(k)(n_(k)) of the other vehicle O_(k)have been completed. Therefore, in this case, the interference levelcalculation part 106 assigns the final level of interference r₁(n₁, k)evaluating interference between the trajectory P₁(n₁) of subject vehicleO₁ and all trajectories of the other vehicle O_(k) (step S433) andoutputs the assigned value to store it in the storage section 108 (stepS434).

Steps S435 to S438 that follow concerns determination processing ofrepetition of Loop2 and Loop1 and are the same as steps S414 to S417 forinterference level calculation processing described for the thirdembodiment.

According to the fourth embodiment of the present invention describedabove, a computer having a storage unit that stores at least thepositions of a plurality of objects and an internal state including thespeed of each object reads the positions and internal states of theplurality of objects from the storage unit, generates trajectories in aspace-time consisting of time and space from changes of the positionsthat can be taken by each of the plurality of objects with the passageof time based on the read positions and internal states of the objects,predicts probabilistically paths of the plurality of objects by usingthe generated trajectory, and based on results of the prediction,calculates the level of interference quantitatively showing an extent ofinterference between trajectories that can be followed by the specificobject in the space-time and those that can be followed by the otherobjects so that safety can be secured even in situations that canactually occur.

Also, according to the fourth embodiment, by applying the level ofinterference defined by using the minimum collision time, possibilitiesof collision with other objects can precisely be predicted within apractical period of time.

Fifth Embodiment

A fifth embodiment of the present invention is characterized in thatinterference between subject vehicle and a surrounding space-timeenvironment is evaluated by summarizing results of interference levelcalculation between subject vehicle and other vehicles obtained in thesame manner as in the third embodiment. The functional configuration ofan object path prediction apparatus according to the fifth embodiment isthe same as that of the object path prediction apparatus 101 accordingto the third embodiment (See FIG. 12). An object path prediction methodaccording to the fifth embodiment is the same as that according to thethird embodiment except interference level calculation processing.

FIG. 20 is a flowchart showing details of interference level calculationprocessing (corresponding to step S34 in FIG. 13) by the object pathprediction method according to the fifth embodiment of the presentinvention. The interference level calculation part 106 first startsrepetitive processing (Loop1) for all trajectories of subject vehicle O₁(step S441). For this purpose, one trajectory is selected from the setof trajectories {P₁ (n₁)} and subsequent processing is performed for theselected trajectory.

In the fifth embodiment, repetitive processing (Loop2) for the othervehicle O_(k), repetitive processing (Loop3) for all elements of a setof trajectories {P_(k)(n_(k))} of the other vehicle O_(k), andrepetitive processing (Loop4) for evaluating interference between thetrajectory P₁(n₁) of subject vehicle O₁ and the trajectory P_(k)(n_(k))of the other vehicle O_(k) are the same as those in the thirdembodiment. That is, processing at steps S442 to S455 shown in FIG. 20is the same as that of steps S402 to S415 (See FIG. 16) described ininterference level calculation processing of the third embodiment.

After repetitive processing of Loop2 terminates, the interference levelcalculation part 106 assigns a weight α(k) (>0) in accordance with theother vehicle O_(k) to the level of interference r₁(n₁, k) obtained fromLoop2 to Loop4, calculates a total level of interference as summation ofthese by:

$\begin{matrix}\left\lbrack {{Formula}\mspace{14mu} 8} \right\rbrack & \; \\{{R_{1}\left( n_{1} \right)} = {\sum\limits_{k = 2}^{K}{{\alpha (k)}{r_{1}\left( {n_{1},k} \right)}}}} & (6)\end{matrix}$

and output a calculation result thereof to store it in the storagesection 108 (step S456). The value of the weight α(k) may all be equaland constant (for example, 1), or a value in accordance with conditionssuch as the type of the other vehicle O_(k) may be assigned. As aresult, it becomes possible to evaluate interference between thetrajectory P₁(n₁) of subject vehicle O₁ and the whole environmentincluding all other vehicles O₂, . . . , O_(k).

The total level of interference R₁(n₁) may be defined by:

$\begin{matrix}\left\lbrack {{Formula}\mspace{14mu} 9} \right\rbrack & \; \\{{R_{1}\left( n_{1} \right)} = {\max\limits_{k}\left( {{\alpha (k)}{r_{1}\left( {n_{1},k} \right)}} \right)}} & (7)\end{matrix}$

In this case, danger of the most dangerous object O_(k) will be handledas the total level of interference. If the definition of Formula (6) isfollowed, there is a possibility that the total level of interference iscalculated to be low when, for example, subject vehicle O₁ interfereswith a small number of objects, but not with many remaining objects.Thus, even a situation in which it is intuitively very dangerous for ahuman because a small number of vehicles run near subject vehicle O₁,there is a possibility that, contrary to intuition, such a situation isdetermined to be safe. On the other hand, by performing interferenceevaluations based on a definition of Formula (7) or the like, apossibility of making a determination to be safe contrary to intuition,as described above, can be reduced.

Subsequently, the interference level calculation part 106 performsdetermination processing whether to repeat Loop1. That is, if thereremains a trajectory of the set of trajectories {P₁(n₁)} of subjectvehicle O₁ whose interference evaluation should be performed (No at stepS457), the interference level calculation part 106 increments the valueof n₁ by 1 (step S458) and repeats Loop1 after returning to step S441.If, on the other hand, there remains no trajectory of the set oftrajectories {P₁(n₁)} of subject vehicle O₁ whose interferenceevaluation should be performed (Yes at step S457), the interferencelevel calculation part 106 terminates Loop1 before terminatinginterference level calculation processing (step S34).

FIG. 21 is a diagram schematically showing the configuration of thespace-time environment to which the object path prediction methodaccording to the fifth embodiment is applied. A space-time environmentEnv(P₁, P₂, P₃) shown in the figure shows a case in which two othervehicles are present with respect to subject vehicle O₁. TrajectoriesP₁(n₁) of subject vehicle O₁ are denoted by solid lines, trajectoriesP₂(n₂) of a second vehicle O₂ are denoted by broken lines, andtrajectories P₃(n₃) of a third vehicle O₃ are denoted by thick lines. Byperforming interference evaluations using the total level ofinterference R₁(n₁) with the space-time environment Env(P₁, P₂, P₃),instead of handling the level of interference r₁(n₁, 2) with the secondvehicle O₂ and the level of interference r₁(n₁, 3) with the thirdvehicle O₃ separately, danger of subject vehicle O₁ can be averted inaccordance with the surrounding environment.

According to the fifth embodiment of the present invention describedabove, a computer having a storage unit that stores at least thepositions of a plurality of objects and an internal state including thespeed of each object reads the positions and internal states of theplurality of objects from the storage unit, generates trajectories in aspace-time consisting of time and space from changes of the positionsthat can be taken by each of the plurality of objects with the passageof time based on the read positions and internal states of the objects,predicts probabilistically paths of the plurality of objects by usingthe generated trajectory, and based on results of the prediction,calculates the level of interference quantitatively showing an extent ofinterference between trajectories that can be followed by the specificobject in the space-time and those that can be followed by the otherobjects so that safety can be secured even in situations that canactually occur.

Also, according to the fifth embodiment, by using the total level ofinterference, interference evaluations can be performed with precisionwhen the number of objects constituting the space-time environment islarge.

Incidentally, also in the fifth embodiment, any of various definitionslike in the third embodiment can be adopted as the value of thecoefficient c_(1k) or F(t) when the level of interference r₁(n₁, k) isincreased due to a collision. The level of interference r₁(n₁, 3) mayalso be defined, like in the fourth embodiment, as the minimum collisiontime.

In addition, when performing interference evaluations in the fifthembodiment, both the total level of interference R₁(n₁) and theindividual level of interference r₁(n₁, k) may be combined forinterference evaluation.

Sixth Embodiment

FIG. 22 is a block diagram showing the functional configuration of anautomatic operation system constituted by using an object pathprediction apparatus according to a sixth embodiment of the presentinvention. An automatic operation system 1000 shown in the figure ismounted on a movable body such as a four-wheel vehicle and includes anobject path prediction apparatus 201 for setting a path to be taken bysubject vehicle by predicting paths that can be taken by subjectvehicle, which is a specific object, and those that can be taken byother objects (including vehicles, people, and obstacles) and anactuator apparatus 211 for operating subject vehicle in accordance withan actuating signal for realizing the path set by the object pathprediction apparatus 201.

The object path prediction apparatus 201 includes the input section 2into which various kinds of information are input from outside, thesensor section 3 for detecting positions and internal states of objectspresent within a predetermined range, the trajectory generation section4 for generating a trajectory in space-time constituted by time andspace from changes of the positions that can be taken by an object withthe passage of time based on results detected by the sensor section 3,the prediction section 105 for making a probabilistic prediction aboutthe path of the object using the trajectory generated by the trajectorygeneration section 4, the interference level calculation part 106 forcalculating the level of interference showing quantitatively an extentof interference between paths that can be taken by subject vehicle andthose that can be taken by other objects based on results of predictionsmade by the prediction section 105, a path selection part 207 forselecting a path that subject vehicle should take in accordance with thelevel of interference calculated by the interference level calculationpart 106, an actuating signal transmission part 208 for transmitting anactuating signal to the actuator apparatus 211 after generating theactuating signal corresponding to a result of selection made by the pathselection part 207, an output section 209 for outputting various kindsof information concerning processing performed by the object pathprediction apparatus 201, and a storage section 210 for storing variouskinds of information including positions and internal states of objectsdetected by the sensor section 3. In FIG. 22, the same referencenumerals are granted to components having the same functionalconfiguration as that of the object path prediction apparatus 101according to the third embodiment as in FIG. 12.

The output section 209 has a display part 291 for displaying/outputtinginformation concerning processing performed by the prediction section105, the interference level calculation part 106, and the path selectionpart 207 as information including images, and a warning beep generationpart 292 for generating a warning beep in accordance with results ofpredictions made by the prediction section 105 or results ofcalculations made by the interference level calculation part 106.

The storage section 210 stores, in addition to detection results by thesensor section 3, trajectories generated by the trajectory generationsection 4, results of predictions made by the prediction section 105,results of interference level calculation by the interference levelcalculation part 106, results of path selection by the path selectionpart 207, operations selected by the operation selection part 41 in thetrajectory generation section 4 and the like.

Next, an object path prediction method according to the sixth embodimentwill be described. FIG. 23 is a flowchart showing the overview ofprocessing of the object path prediction method according to the sixthembodiment. Also in the sixth embodiment, all objects to be predictedare assumed to move on a two-dimensional plane for a description.

In the sixth embodiment, detecting processing of the position andinternal state of each object (step S61), trajectory generationprocessing for each object in a space-time (step S62), probabilisticprediction processing of object paths using trajectories (step S63), andinterference level calculation processing between subject vehicle andother vehicles based on prediction results (step S64) are the same asstep S31, step S32, step S33, and step S34 described in the thirdembodiment respectively. Processing at step S65 and thereafter will bedescribed below in detail. At step S65, path selection processing inaccordance with the level of interference calculated in interferencelevel calculation processing at step S64 is performed (step S65). In theautomatic operation technology of a movable body that moves in a widerange such as an automobile, as described above, in addition to the pathfinding technology in which an influence of at least other dynamicobstacles is not considered or an influence thereof is not practicallyneeded for calculation, a path calculation technology by whichcalculation needed for avoiding collision with dynamic obstacles isrealized within a practical time to avoid danger while running isneeded. In the sixth embodiment, two evaluation values are used in pathselection processing to avert danger. The first evaluation value is thelevel of interference calculated in interference level calculationprocessing and the first path selection processing is performed by usingthe level of interference. If a plurality of paths is selected as aresult of the first path selection processing, the second evaluationvalue stored in the storage section 210 is used to perform the secondpath selection processing. In the second path selection processing, itis preferable to use, in addition to a selection criterion by which apath contains components along a path to a destination, an additionalselection criterion (described later) to further narrow down paths incombination as the second evaluation value when appropriate.

By evaluating the levels of interference between sets of trajectories ofother vehicles and a set of trajectories of subject vehicle withendpoints different from each other and selecting paths in accordancewith the evaluated levels of interference, as described above, anoptimal path is determined each time without presetting a location suchas a destination to be reached by subject vehicle to precisely avertdanger while subject vehicle travels so that safety can be secured (SeeFIG. 15). As a result, a problem shown in FIG. 14, that is, a fatalproblem that safety is not secured even if subject vehicle travelstoward a preset location on a road can be solved.

FIG. 24 is a flowchart showing details of path selection processing. InFIG. 24, the path selection part 207 selects a trajectory whose value ofthe level of interference r₁(n₁, k) calculated in interference levelcalculation processing is minimum (step S501).

If, as a result of selecting a trajectory whose level of interferencer₁(n₁, k) is minimum, only one trajectory remains (No at step S502), thepath selection part 207 reads a history of the positions (x(t), y(t))corresponding to the selected trajectory and the operation sequence{u(t)} at t=0 to T from the storage section 210 and outputs them to theactuating signal transmission part 208 (step S504). In contrast, if, asa result of selecting a trajectory whose level of interference r₁(n₁, k)is minimum, a plurality of trajectories remains (Yes in step S502), thepath selection part 207 proceeds to step S503.

At step S503, the path selection part 207 selects a trajectory that bestmatches the additional selection criterion from among a plurality oftrajectories selected at step S501 by using the selection criterion bywhich a path contains components along a path to a destination and theadditional selection criterion which is previously set and stored instorage section 210 (step S503). A condition that has almost nopossibility to have a duplicate value among trajectories may be set asthe additional selection criterion.

Some examples of the additional selection criterion are given below:

(1) Among positions of subject vehicle O₁ after an operation (after Δt),a position (x coordinate in FIG. 4 and the like) in a road widthdirection that is nearest to the center of the lane on which subjectvehicle travels. In this case, a trajectory for running the mostdesirable position on the road is selected. If a trajectory that takesthe most stable positioning during t=0 to T is selected, a trajectorymay be selected so that the sum of the position in the road widthdirection after each operation at t=0 to T is minimum.

(2) Among positions of subject vehicle O₁ after an operation (after Δt),a position in a traveling direction (y coordinate direction in FIG. 4and the like) is maximal. In this case, the fastest trajectory isselected. Or, a trajectory whose position at t=T is maximal may also beselected.

(3) The magnitude of acceleration at the initial time (t=0) is minimum.In this case, a trajectory whose acceleration is most smooth isselected. If a trajectory whose acceleration is most smooth in theoperation sequence at t=0 to T is selected, a trajectory may be selectedso that the sum of the magnitude of acceleration after each operation att=0 to T is minimum.

(4) The magnitude of angular velocity at the initial time (t=0) isminimum. In this case, a trajectory whose steering is most smooth isselected. If a trajectory whose steering is most smooth in the operationsequence at t=0 to T is selected, like (3), a trajectory may be selectedso that the sum of the magnitude of angular velocity after eachoperation at t=0 to T is maximal.

According to the path selection processing described above, by selectinga trajectory most likely to avert danger of subject vehicle O₁ as atrajectory in a space-time, a path that subject vehicle O₁ should betaken on an actual two-dimensional plane will, as a result, have beenselected.

If only one trajectory remains at step S503, the path selection part 207proceeds to step S504 described above. If a plurality of trajectoriesstill remains even after applying the additional selection criterion,for example, a setting can be made so that a trajectory for which thevalue of the counter n₁ or k takes a minimal or maximal value isautomatically selected.

Subsequent to the path selection processing described above, theactuating signal transmission part 208 generates a history of positions(x(t), y(t)) corresponding to the trajectory output in accordance with aresult of selection at step S65 and an actuating signal in accordancewith the operation sequence {u(t)} at t=0 to T and transmits them to theactuator apparatus 211 (step S66).

The actuating signal generated and transmitted by the actuating signaltransmission part 208 at step S66 depends on the configuration of theactuator apparatus 211. For example, if the actuator apparatus 211 is amechanical apparatus such as a steering gear, an accelerator, and abrake, the actuating signal transmission part 208 can output a historyof positions (x(t), y(t)) received from path selection part 207 andoperations {u(t)} unchanged as an actuating signal.

In contrast, if the actuator apparatus 211 is an apparatus for addingoperating torque to a mechanical apparatus such as a steering gear, anaccelerator, and a brake, the actuating signal transmission part 208 maytransmit a history of positions received from path selection part 207(x(t), y(t)) and operations {u(t)} unchanged as an actuating signal oran operating torque to be added to such a mechanical apparatus duringoperation after calculating it. In the former case, an operating torquewill be calculated on the actuator apparatus 211 side.

If the actuator apparatus 211 is an apparatus for adding operatingtorque, and the driver can switch to a manual operation by adding anoperating torque greater than that of the actuator apparatus 211, thatis, the apparatus can be overridden by the driver, the automaticoperation system 1000 can also be applied as an auxiliary apparatus ofoperation so that, while a path is selected, an operation reflecting thedriver's intention can be realized.

Incidentally, by detecting conditions of the road surface on whichsubject vehicle O₁ travels using the sensor section 3, the actuatorapparatus 211 may be controlled based on feedback in accordance with theconditions of the road surface.

FIG. 25 is a diagram showing a display output example of path selectionresults or the like in the display part 291 of the output section 209and a diagram schematically showing a display output example when a pathselection is made in a space-time environment Env′ (P₁, P₂) (See FIG. 6)constituted by subject vehicle O₁ and another vehicle O₂. Morespecifically, FIG. 25 shows a case in which a setup path is displayed bysuperimposition on the front glass F with a translucent arrow H and alsoan area where, among paths that can be taken by subject vehicle O₁ on atwo-dimensional plane in accordance with the level of interferencer₁(n₁, 2) between subject vehicle O₁ and the other vehicle O₂, the valueof the level of interference r₁(n₁, 2) thereof exceeds a presetthreshold is translucently displayed by superimposition on the frontglass F of the object O₁ (subject vehicle). The case shown in FIG. 25schematically shows a situation in which the actuator apparatus 211causes steering ST to generate operating torque so that operating torqueto rotate the steering ST clockwise is generated in order to take asetup path.

Also in FIG. 25, two areas translucently displayed, the area D_(a) andthe area D_(b), have different illumination (Here, the area D_(a) isbrighter). Such a difference in illumination corresponds to a differenceof values of the level of interference r₁(n₁, 2) and the illuminationsignifies that the value of the level of interference r₁(n₁, 2)increases if a path approaching the area D_(a) is selected. In thissense, FIG. 25 visually indicates that taking a path to the area D_(b)with a relatively smaller value of the level of interference r₁(n₁, 2)enables the drive to drive in order to avert danger.

In addition to displaying by the display part 291, the warning beepgeneration part 292 may generate a warning beep (including voice) whenthe value of the level of interference r₁(n₁, 2) obtained for ananticipated path in accordance with the current operation exceeds apredetermined threshold.

The display output example in the output section 209 is not limited tothis and, for example, a setup path and interference evaluation resultsmay be displayed by causing the display screen CN (See FIG. 26) of a carnavigation system to have the function of the display part 291. In thiscase, as shown in FIG. 26, the setup path is displayed by the arrow Hand also a difference of the level of interference between the twoareas, the area D_(a) and the area D_(b), is displayed by gradations incolor for each area on a two-dimensional plane displayed on the displayscreen CN.

According to the sixth embodiment of the present invention describedabove, a computer having a storage unit that stores at least thepositions of a plurality of objects and an internal state including thespeed of each object reads the positions and internal states of theplurality of objects from the storage unit, generates trajectories in aspace-time consisting of time and space from changes of the positionsthat can be taken by each of the plurality of objects with the passageof time based on the read positions and internal states of the objects,predicts probabilistically paths of the plurality of objects by usingthe generated trajectory, calculates the level of interferencequantitatively showing an extent of interference between paths that canbe taken by the specific object and those that can be taken by the otherobjects based on results of the prediction, and selects the path to betaken by the specific object in accordance with the calculated level ofinterference within a practical time so that safety can be secured evenin situations that can actually occur.

Also, according to the sixth embodiment, by applying the level ofinterference defined by using the collision probability in a space-timebetween a specific object and other objects and selecting a trajectorywhose level of interference is minimum in the space-time, a path, amongpaths that can be taken by the specific object, whose possibility ofcollision with other objects is the lowest can be set with precision.

Further, according to the sixth embodiment, by making path predictionsof objects using a space-time environment formed in a space-timeconsisting of time and space, path predictions of not only staticobjects, but also dynamic objects can be made with precision.

In addition, according to the sixth embodiment, since trajectories ofdetected objects are generated independently, a specific object (forexample, subject vehicle) can be distinguished from other objects. As aresult, danger that may lurk between the specific object and the otherobjects can be predicted easily and precisely.

Incidentally, in the sixth embodiment, the coefficient c_(1k) in Formula(5) for increasing the value of the level of interference r₁(n₁, k) maynot be constant, and for example, the coefficient c_(1k) may be amagnitude of relative velocity between subject vehicle O₁ and the othervehicle O_(k) at the time of collision. Or, a value indicatingseriousness of damage may be assigned to the coefficient c_(1k).

Also in the sixth embodiment, the level of interference may becalculated by assuming that an operation of subject vehicle O₁ ismaintained. Situations can thereby be simplified for prediction when,for example, there are many surrounding objects, leading to reducedcalculated amounts in the trajectory generation section, the predictionsection, the interference level calculation part and the path selectionpart.

Seventh Embodiment

A seventh embodiment of the present invention is characterized in thatinterference between subject vehicle and a surrounding space-timeenvironment is evaluated by summarizing results of interference levelcalculation between subject vehicle (specific object) and other vehiclesobtained in the same manner as in the sixth embodiment and a path to betaken by subject vehicle is selected based on the evaluation result. Thefunctional configuration of an object path prediction apparatusaccording to the seventh embodiment is the same as that of the objectpath prediction apparatus 201 according to the sixth embodiment (SeeFIG. 22). An object path prediction method according to the seventhembodiment is the same as that according to the sixth embodiment exceptinterference level calculation processing.

FIG. 27 is a flowchart showing details of interference level calculationprocessing (corresponding to step S64 in FIG. 23) by the object pathprediction method according to the seventh embodiment. The interferencelevel calculation part 106 first starts repetitive processing (Loop1)for all trajectories of subject vehicle O₁(step S461). For this purpose,one trajectory is selected from the set of trajectories {P₁(n₁)} andsubsequent processing is performed for the selected trajectory.

Then, processing at steps S462 to S472 performed by the interferencelevel calculation part 106 is the same as that at steps S422 to S432described in the fourth embodiment. Thus, processing at step S473 andthereafter will be described below.

Step S473 is performed when repetition of Loop 3 is completed (Yes atstep S471), that is, all interference evaluations with one trajectoryP₁(n₁) of subject vehicle O₁ among trajectories generated with respectto the other vehicle O_(k) are performed. With this step S473, theinterference level calculation part 106 will have completed interferenceevaluations of one trajectory P_(k)(n_(k)) of the other vehicle O_(k).Therefore, in this case, the interference level calculation part 106assigns the level of interference r₁(n₁, k) evaluating interferencebetween the trajectory P₁(n₁) of subject vehicle O₁ and all trajectoriesof the other vehicle O_(k) (step S473) and outputs the assigned value tostore it in the storage section 210 (step S474).

Then, the interference level calculation part 106 performs determinationprocessing whether to repeat Loop2. If there remains another vehicleO_(k) whose interference evaluation with subject vehicle O₁ should beperformed (No at step S475), the interference level calculation part 106increments the value of k by 1 (step S476) and repeats Loop2 afterreturning to step S462. If, on the other hand, there remains no vehicleO_(k) whose interference evaluation with subject vehicle O₁ should beperformed (Yes at step S475), the interference level calculation part106 proceeds to the subsequent step S477 after completing repetition ofLoop2.

At step S477, the interference level calculation part 106 calculates thetotal level of interference R₁(n₁) given by Formula (6) using the levelsof interference r₁(n₁, k) obtained in Loop2 to Loop4 and output a resultof the calculation to store it in the storage section 210 (step S477).Incidentally, Formula (7) may also be adopted as the total level ofinterference R₁(n₁).

Subsequently, the interference level calculation part 106 performsdetermination processing whether to repeat Loop1. That is, if thereremains a trajectory of the set of trajectories {P₁(n₁)} of subjectvehicle O₁ whose interference evaluation should be performed (No at stepS478), the interference level calculation part 106 increments the valueof n₁ by 1 (step S479) and repeats Loop1 after returning to step S461.If, on the other hand, there remains no trajectory of the set oftrajectories {P₁(n₁)} of subject vehicle O₁ whose interferenceevaluation should be performed (Yes at step S478), the interferencelevel calculation part 106 terminates Loop1 before terminatinginterference level calculation processing (step S64).

Next, path selection processing (corresponding to step S65 in FIG. 23)will be described. FIG. 28 is a flowchart showing details of pathselection processing by the object path prediction method according tothe seventh embodiment. In FIG. 28, the path selection part 207 selectsa trajectory whose total level of interference R₁(n₁) calculated in theinterference level calculation processing is maximal (step S511).

If, as a result of selecting a trajectory whose level of interference ismaximal, only one trajectory remains (No at step S512), the pathselection part 207 reads a history of the positions (x(t), y(t))corresponding to the selected trajectory and the operation sequence{u(t)} at t=0 to T from the storage section 210 and outputs them to theactuating signal transmission part 208 (step S514). In contrast, if, asa result of selecting a trajectory whose level of interference ismaximal, a plurality of trajectories remains (Yes in step S512), thepath selection part 207 proceeds to step S513.

At step S513, the path selection part 207 selects a trajectory that bestmatches an additional selection criterion from among a plurality oftrajectories selected at step S511 by using the preset selectioncriterion stored in the storage section 210 (step S513). A conditionlike that in the sixth embodiment may be set as the additional selectioncriterion.

If only one trajectory remains at step S513, the path selection part 207proceeds to step S514 described above. If a plurality of trajectoriesstill remains even after applying the additional selection criterion,for example, a setting can be made so that a trajectory for which thevalue of the counter n₁ or k takes a minimal or maximal value isautomatically selected.

According to the path selection processing described above, by selectinga trajectory most likely to avert danger of subject vehicle O₁ as atrajectory in a space-time, a path that should be taken on an actualtwo-dimensional plane will, as a result, have been selected by subjectvehicle O₁.

Processing (step S66) by the actuating signal transmission part 208subsequent to the path selection processing at step S65 is the same asthat in the sixth embodiment. Also, processing by the actuator apparatus211 after receiving an actuating signal from the actuating signaltransmission part 208 is the same as that in the sixth embodiment.

According to the seventh embodiment of the present invention describedabove, a computer having a storage unit that stores at least thepositions of a plurality of objects and an internal state including thespeed of each object reads the positions and internal states of theplurality of objects from the storage unit, generates trajectories in aspace-time consisting of time and space from changes of the positionsthat can be taken by each of the plurality of objects with the passageof time based on the read positions and internal states of the objects,predicts probabilistically paths of the plurality of objects by usingthe generated trajectory, calculates the level of interferencequantitatively showing an extent of interference between paths that canbe taken by the specific object and those that can be taken by the otherobjects based on results of the prediction, and selects the path to betaken by the specific object in accordance with the calculated level ofinterference within a practical time so that, as same in the sixthembodiment, safety can be secured even in situations that can actuallyoccur.

Also, according to the seventh embodiment, by applying the total levelof interference obtained by adding up the level of interference definedby using the minimum collision time after assigning weights for eachobject and selecting a trajectory whose total level of interference ismaximal in a space-time, a path whose possibility of collision withother objects is the lowest can be set with precision even when thenumber of objects constituting the space-time environment is large.

In the seventh embodiment, the level of interference r₁(n₁, k) may bedefined like in the sixth embodiment and any of various definitions likein the third embodiment can be adopted as the value of the coefficientc_(1k) or F(t) when the level of interference r₁(n₁, k) is increased dueto a collision. In this case, a trajectory whose R₁(n₁) becomes minimumcan be selected in the path selection processing.

In addition, also when performing interference evaluations in theseventh embodiment, both the total level of interference R₁(n₁) and theindividual level of interference r₁(n₁, k) may be combined forinterference evaluation.

Other Embodiments

So far, the first to seventh embodiments have been described in detailas best modes for carrying out the present invention, but the presentinvention is not limited to these embodiments. For example, in an objectpath prediction method according to the present invention, by arranging,in addition to existing objects detected by a sensor section, virtualobjects, path predictions of the arranged virtual objects may be made.More specifically, path predictions may be made by constructing avirtual object model exhibiting behavior unfavorable to subject vehicleand arranging the object model at a predetermined position. Using such avirtual object model, when making path predictions of the vehicle(subject vehicle) running near an intersection that does not offer awide view due to presence of, for example, a shelter, it becomespossible to predict danger of collision or the like that could fly outfrom the intersection by arranging the model at a position undetectablefrom subject vehicle. Information concerning a virtual object model maybe arranged at a desired position in accordance with condition settingsfrom an input section after storing the information in a storage sectionin advance.

When an object path prediction apparatus according to the presentinvention is applied to a field such as an expressway where only runningvehicles are assumed, by causing each vehicle to have a communicationsmeans for inter-vehicle communication, vehicles running nearby mayexchange running conditions mutually by means of inter-vehiclecommunication. In this case, information concerning an operationselection probability may be transmitted to other vehicles by storing ahistory of operations of each vehicle in a storage section thereof andattaching an operation selection probability of each operation based onthe history of operations. Precision of path prediction is therebyenhanced so that danger while running can more reliably be averted.

Further, in the present invention, a GPS (Global Positioning System) canbe employed as a position detecting unit. In such a case, positioninformation and movement information of objects detected by a sensorsection can be corrected by referencing three-dimensional mapinformation stored in the GPS. Further, the GPS can be caused tofunction as a sensor section by mutual communication of output of theGPS. In all cases, high-precision path predictions can be realized byemploying the GPS, further improving reliability of prediction results.

The present invention is applicable to objects moving in thethree-dimensional space. The present invention is also applicable to anobject having a plurality of levels of freedom (for example, an objectlike a robot arm having six levels of freedom).

As is evident from descriptions above, the present invention can includevarious embodiments not described here and various design modificationscan be made without departing from the scope of technological principlesspecified by appended claims.

INDUSTRIAL APPLICABILITY

An object path prediction method, apparatus, and program and anautomatic operation system according to the present invention issuitable as a technology to secure safety by averting danger whiledriving a movable body such as a four-wheel vehicle.

1. An object path prediction method for predicting a path of an objectby a computer having a storage unit that stores at least a position ofthe object and an internal state including a speed of the object,comprising: a trajectory generation step of generating a trajectory in aspace-time constituted by time and space from changes of the positionthat can be taken by the object with a passage of time based on a readposition and internal state of the object after reading the position andinternal state of the object from the storage unit; and a predictionstep of probabilistically predicting the path of the object by using thetrajectory generated in the trajectory generation step.
 2. The objectpath prediction method according to claim 1, wherein the trajectorygeneration step includes an operation selection step of selecting anoperation performed on the object from a plurality of operations, anobject operation step of causing the operation selected in the operationselection step to be performed for a predetermined period of time, and adetermination step of determining whether or not the position and theinternal state of the object after the operation selected is performedin the object operation step satisfy control conditions concerningcontrol of the object and movement conditions concerning movable areasof the object, wherein a set of processing from the operation selectionstep to the determination step is repeatedly performed until atrajectory generation time in which the trajectory is generated hasreached.
 3. The object path prediction method according to claim 2,wherein the operation selection step selects an operation in accordancewith an operation selection probability granted to each of the pluralityof operations, and if, as a result of determination in the determinationstep, the position and the internal state of the object satisfy thecontrol conditions and the movement conditions, the time is set forwardbefore returning to the operation selection step. 4-9. (canceled)
 10. Anobject path prediction method for predicting paths of a plurality ofobjects by a computer having a storage unit that stores at leastpositions of the plurality of objects and an internal state including aspeed of each object, comprising: a trajectory generation step ofgenerating a trajectory in a space-time constituted by time and spacefrom changes of the position that can be taken by each of the pluralityof objects with a passage of time based on, after reading the positionsand the internal states of the plurality of objects from the storageunit, the read positions and the internal states of the objects; aprediction step of probabilistically predicting the paths of theplurality of objects by using the trajectories generated in thetrajectory generation step; and an interference level calculation stepof calculating, based on results of prediction in the prediction step, alevel of interference quantitatively showing an extent of interferencebetween the paths that can be taken by a specific object and those thatcan be taken by other objects.
 11. The object path prediction methodaccording to claim 10, wherein the interference level calculation stepincreases or decreases a value of the level of interference between thespecific object and each of the other objects by a specified quantity inaccordance with a number of times that the specific object and each ofthe other objects move closer than an interference distance, which is aspatial distance at which objects interfere with each other.
 12. Theobject path prediction method according to claim 11, wherein theinterference level calculation step increases, when the specific objectand one of the other objects move closer than the interference distance,the value of the level of interference between both objects movingcloser in proportion to a collision probability of the both objects inthe space-time.
 13. The object path prediction method according to claim11, wherein the interference level calculation step increases, when thespecific object and one of the other objects move closer than theinterference distance, the value of the level of interference betweenboth objects moving closer in proportion to a magnitude of a relativevelocity at a time when the both objects move closer.
 14. The objectpath prediction method according to claim 11, wherein the storage unitstores a magnitude of a relative velocity during collision betweendifferent objects by associating with a damage scale evaluation valuefor evaluating a scale of damage caused by the collision or an amount ofdamage losses caused by the collision and the interference levelcalculation step reads, when the specific object and one of the otherobjects move closer than the interference distance, the damage scaleevaluation value or the amount of damage losses in accordance with themagnitude of the relative velocity at a time when both objects movecloser from the storage unit and increases the level of interferencebetween the both objects in proportion to the damage scale evaluationvalue or the amount of damage losses. 15-16. (canceled)
 17. The objectpath prediction method according to claim 10, wherein the trajectorygeneration step includes an operation selection step of selecting anoperation performed on the object from a plurality of operations, anobject operation step of causing the operation selected in the operationselection step to be performed for a predetermined period of time, and adetermination step of determining whether or not the position and theinternal state of the object after the operation selected is performedin the object operation step satisfy control conditions concerningcontrol of the object and movement conditions concerning movable areasof the object, wherein a set of processing from the operation selectionstep to the determination step is repeatedly performed until atrajectory generation time in which the trajectory is generated hasreached. 18-21. (canceled)
 22. The object path prediction methodaccording to claim 10, further comprising a path selection step ofselecting a path to be taken by the specific object contained in theplurality of objects in accordance with the level of interferencecalculated in the interference level calculation step. 23-24. (canceled)25. The object path prediction method according to claim 22, wherein thelevel of interference has a larger value as an extent of interferencebetween paths that can be taken by the specific object and those thatcan be taken by the other objects decreases and the path selection stepselects a path whose level of interference is maximal. 26-29. (canceled)30. The object path prediction method according to claim 22, wherein thetrajectory generation step includes an operation selection step ofselecting an operation performed on the object from a plurality ofoperations, an object operation step of causing the operation selectedin the operation selection step to be performed for a predeterminedperiod of time, and a determination step of determining whether or notthe position and the internal state of the object after the operationselected is performed in the object operation step satisfy controlconditions concerning control of the object and movement conditionsconcerning movable areas of the object, wherein a set of processing fromthe operation selection step to the determination step is repeatedlyperformed until a trajectory generation time in which the trajectory isgenerated has reached.
 31. (canceled)
 32. An object path predictionapparatus, comprising: a storage unit that stores at least a position ofan object and an internal state including a speed of the object; atrajectory generation unit that generates a trajectory in a space-timeconstituted by time and space from changes of the position that can betaken by the object with a passage of time based on a read position andinternal state of the object after reading the position and the internalstate of the object from the storage unit; and a prediction unit thatprobabilistically predicts the path of the object by using thetrajectory generated by the trajectory generation unit. 33-40.(canceled)
 41. An object path prediction apparatus, comprising: astorage unit that stores at least positions of a plurality of objectsand an internal state including a speed of each object; a trajectorygeneration unit that generates a trajectory in a space-time constitutedby time and space from changes of the position that can be taken by eachof the plurality of objects with a passage of time based on, afterreading the positions and the internal states of the plurality ofobjects from the storage unit, the read positions and the internalstates of the objects; a prediction unit that probabilistically predictsthe paths of the plurality of objects by using the trajectoriesgenerated by the trajectory generation unit; and an interference levelcalculation unit that calculates, based on results of prediction by theprediction unit, a level of interference quantitatively showing anextent of interference between the paths that can be taken by a specificobject and those that can be taken by other objects. 42-52. (canceled)53. The object path prediction apparatus according to claim 41, furthercomprising a path selection unit that selects a path to be taken by thespecific object in accordance with the level of interference calculatedby the interference level calculation unit. 54-62. (canceled)
 63. Acomputer-readable recording medium that stores therein a computerprogram for performing an object path prediction, a computer programcausing a computer to execute the object path prediction methodaccording to claim
 1. 64. (canceled)
 65. A computer-readable recordingmedium that stores therein a computer program for performing an objectpath prediction, the computer program causing a computer to execute theobject path prediction method according to claim 10.